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Introduction

A bot text corpus is a collection of texts generated by a bot, a software program designed to perform automated tasks, such as responding to user input or scraping data from the internet.

On the other hand, a human text corpus is a collection of texts humans have written. Human texts may be written in various styles and formats, including narrative, argumentative, descriptive, etc.

The main difference between bot texts and human texts is that a machine generates the bot text, while humans write the latter.

Our DataHour speaker, Sumeet, will give you a practical walkthrough on a collection of Human Text Corpus for bilinguals (English and Hindi) and apply pre-processing techniques to clean it.

About Expert: Sumeet Lalla, Data Scientist at Cognizant, has completed his Masters in Data Science from the Higher School of Economics Moscow and Bachelor of Engineering in Computer Engineering from Thapar University. With 5.5 years of experience in Data Science and Software Engineering, he currently works as a Data Scientist at Cognizant.

Distinguishing Bot Text from Human Text Corpus

Firstly, we need to collect and pre-process the English/Hindi text. We are using the Gutenberg API to collect English literature novel indices. We are using a jupyter notebook. Side by side, using an ITK and Spacey to do our pre-processing. Now, install tokenizers like “stopwords” and “wordnet” for lemmatization. We have initialized all of these. As you can see below, there is a gutendex web API from which we can pass the relevant parameters.

We would use ‘gutenberg_cleaner’ in the python library to clean irrelevant headers and volumes. Because we only require the text, chapter names, and the title of the book here. Collect all this information in a separate folder. Now for pre-processing, we will remove all the phrases which are not required, like won’t, can’t, etc., will not, and cannot, respectively. It is needed to clean the text. Also, capitalize the first letter of the sentence. We are using NLP for this. 

We need to create a post dictionary so that the person’s name can be substituted with real names. This is part of speech tagging. As you can see below, here we are initializing the multiprocessing. We are using the pool and map to do it. We are getting the CPU count using “multiprocessing.cpu_count”. This will run the pre-processing function which we discussed earlier.

We are creating a corpus for this file. We would append all the previously cleaned elements we had done into the single output file, which is named “english_corpus.txt.” 

Now we are using John Snow spark NLP as it has a pre-trained pipeline for lemmatization and tokenization. Using pyspark here and setting up the document assembler for setting up the tokenizer. We are using a Hindi pretend symmetrized and will get the Hindi text as required. We need to do some manual work here. 

Here, we have created “get_lemmatized_file” for the pre-processing. Same as the English text, here we are creating an NLP spark pipeline and would select the final column as the finished lemma. This would be the final process text. This should be done for all the hindi files. 

Now going back to English again, We need to apply TF-IDF and SVD to generate word vectors. First, we would perform TF Vectorisation of pre-processed text. For that, you need to put an analyzer as a word. SVD is used to reduce the dimensionality of the TF-IDF matrix. Now we have to choose a low-rank k approximation using the Eckart-Young theorem. 

The below slide explains the SVD used for low-rank k approximation to get word vectors.

Basic definitions of SVD.

We can find the value of A using the Eckart-Young-Mirsky Theorem. 

For our approach, it came out to be 10. We are decomposing the English vectors into u, sigma and vt matrix. You will get the row-subspace of the matrix.

Word vector space from reduced SVD Matrix:

For our data, we have u-sigma as row-subspace to represent the whole word vectors in the space. We are using binary search to get the English vectors. Those vectors will be stored in the dictionary for faster use. We need to do stripping for pre-processing and then append it to the file and dictionary. So basically, we will search for the word in the dictionary and get the corresponding vectors. As you can see in the screenshot below, the ‘Speak’ word is represented as a dimension with a column dimension of 10. 

Now, we are moving to the next step, which is generating the n-grams. This is the simplest way of getting the word vectors. To generate n-gram vectors, we have helper functions. Need to give range to the n-grams function. 

Below is the pre-processing and creation of word vectors for English. We will use a similar process that can be followed for Hindi also. 

Bot Text Generation

Coming to the bot text generation, we first have to create the English dictionary from the corpus. We will get the characters to index arrays. It will help in getting the list of unique characters. Now set the SEQ_LEN as 256 and BATCH_SIZE as 16. We have a Prediction length to be 200 by default. Temperature parameters (temp. Is 0.3) will control the randomness of prediction by our models. If the temperature is lower, then the prediction will be less random and more accurate. 

Additionally, Layers would come into action in the forward method as we have to do some squeeze and un-squeeze because we have to compress some dimensions and elongate them based on the input sequence. Lastly, we will initialize the hidden layer, and it will be vector 0 0. The Dimensions will be hidden. We are moving it to devices like CPU and GPU. We are using torch.device and checking if we have a GPU available or not. Initialize the character level RNN with required hyperparameters. We have selected the first line from the human corpus, which is the input sequence for getting the predicted sequence from our character level.

We would create the training procedure by initializing our criteria. Also, it is a cross-entropy loss. We are passing the vector of indexes of characters that can be treated as the label encoding. The number of epochs is chosen as 10,000. We will train the model and then get the predicted output. Then, we can calculate the loss and backward Gradient using it. Set it up to Zero Gradient for evaluation. 

Also, after the model has been trained, we can pass it on to the Evaluate function. We will use both characters to index the dictionary and index to character dictionary. The start text will be the first line of the English corpus or any random line. The prediction length can also be set up to 200 by default. The same pipeline could be used for Hindi also, but encoding would be utf-8. So after generating the bot files, we have to pass it to pre-processing steps and generation of n-gram vectors. 

Now coming for the next step of Clustering those word vectors. We can use k-means or any density-based clustering method. 

Now, we would set up the rapid cuda libraries. For hyperparameter tuning, we’re using tuna. It’s a type of basin optimization technique where we can use a TP sampler, grid samplers, and random samplers. We are proceeding with base n1 because it is faster. The cluster selection method for hdb scan in Cuda currently supports eom excess of mass. We are performing hbd scan on the best hyperparameters obtained above. Then we did the clustering procedure and got word vectors that are present in this cluster. Once we get the bot text for the human and bot corpus, we will compute some cluster matrix. However, one of the matrix will be computing the average distance in a cluster and dividing it by the number of combinations.

We are using p-distance, the optimized computation pairwise for a given matrix. Now append those average distances of the clusters, and another metric would be counting the unique vectors. Let’s store all of this in a list for the human and bot corpus. Our null hypothesis is that if human and bot text corpuses are coming from the same population or not, an alternative is that they belong to different populations. We are selecting significant level alpha=0.05 and running the statistical test for 2 cluster metric lists which we obtained. By computing the p-value, if p is less than the significant level, our results will be statistically significant. We can reject the null hypothesis in favor of an alternate hypothesis. Before performing these steps, we will remove the noise labels. 

Conclusion

We observed that basically p-value of the test is less than or equal to 0.05. Thus, the null hypothesis should favor the alternative hypothesis.

Additionally, our results are statistically significant.

Our Experiment has sufficient evidence to support the two cluster metric distributions derived from different populations. 

The media shown in this article is not owned by Analytics Vidhya and is used from the presenter’s presentation.

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Beginner’s Guide To Image And Text Similarity

Introduction

Image Analysis and Mapping in Earth Engine Using NDVI“, now it is another article about image analysis again. Unlike the previous article, this article discusses general image analysis, not satellite image analysis. The goal of this discussion is to detect whether two products are the same or not. Each of the two products has image and text names. If the pair of products have similar or the same images or text names, that means that the two products are the same. The data comes from a competition held in Kaggle.

# import packages import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from PIL import Image import imagehash from fuzzywuzzy import fuzz from chúng tôi import DecisionTreeClassifier from sklearn import metrics

 

Image Similarity

The similarity of the two images is detected using the package “imagehash”. If two images are identical or almost identical, the imagehash difference will be 0. Two images are more similar if the imagehash difference is closer to 0.

Comparing the similarity of two images using imagehash consists of 5 steps. (1) The images are converted into greyscale. (2) The image sizes are reduced to be smaller, for example, into 8×8 pixels by default. (3) The average value of the 64 pixels is computed. (4)The 64 pixels are checked whether they are bigger than the average value. Now, each of the 64 pixels has a boolean value of true or false. (5) Imagehash difference is the number of different values between the two images. Please observe the below illustration.

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The imagehash difference of the two images/matrices above is 3. It means that there are 3 pixels with different boolean values. The two images are relatively similar.

For more clarity, let’s examine imagehash applied to the following 3 pairs of images. The first pair consists of two same images and the imagehash difference is 0. The second pair compares two similar images. The second image (image_b) is actually an edited version of the first image (image_a). The imagehash difference is 6. The last pair shows the comparison of two totally different images. The imagehash difference is 30, which is the farthest from 0.

Fig. 1 imagehash

# First pair

hash1 = imagehash.average_hash(Image.open('D: /image_a.jpg')) hash2 = imagehash.average_hash(Image.open('D:/ image_a.jpg')) diff = hash1 - hash2 print(diff) # 0 # Second pair hash1 = imagehash.average_hash(Image.open('D: /image_a.jpg')) hash2 = imagehash.average_hash(Image.open('D:/ image_b.jpg')) diff = hash1 - hash2 print(diff) # 6 # Third pair hash1 = imagehash.average_hash(Image.open('D: /image_a.jpg')) hash2 = imagehash.average_hash(Image.open('D:/ image_c.jpg')) diff = hash1 - hash2 print(diff) # 30

Here is how the average imagehash looks like

array([[ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [False, True, False, False, False, False, False, False], [ True, True, False, False, False, False, False, False], [False, False, False, True, False, False, False, False], [False, False, False, True, False, False, False, False], [False, False, False, False, False, False, False, False]])

array([[ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [False, True, True, True, True, False, False, False], [ True, True, True, False, False, False, False, False], [ True, True, False, False, False, False, False, False], [False, False, False, True, False, False, False, False], [False, False, False, True, False, False, False, False], [False, False, False, False, False, False, False, False]])

array([[False, False, False, False, False, False, False, False], [ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [ True, True, True, True, True, True, True, True], [False, False, False, False, True, False, False, False], [False, False, False, False, False, False, False, False]])

Text Similarity

Text similarity can be assessed using Natural Language Processing (NLP). There are 4 ways to compare the similarity of a pair of texts provided by “fuzzywuzzy” package. The function of this package returns an integer value from 0 to 100. The higher value means the higher similarity.

1. fuzz.ratio – is the most simple comparison of the texts. The fuzz.ratio value of “blue shirt” and “blue shirt.” is 95. It means that the two texts are similar or almost the same, but the dot makes them a bit different



The measurement is based on the Levenshtein distance (named after Vladimir Levenshtein). Levenshtein distance measures how similar two texts are. It measures the number of minimum edits, such as inserting, deleting, or substituting, a text into another text. The text “Blue shirt” requires only 1 editing away to be “blue shirt.”. It only needs a single dot to be the same. Hence, the Levenshtein distance is “1”. The fuzz.ratio is calculated with this equation (len(a) + len(b) – lev)/( (len(a) + len(b), where len(a) and len(b) are the lengths of the first and second text, and lev is the Levenshtein distance. The ratio is (10 + 11 – 1)/(10 + 11) = 0.95 or 95%.

2. fuzz.partial_ratio – can detect if a text is a part of another text. But, it cannot detect if the text is in a different order. The example below shows that “blue shirt” is a part of “clean blue shirt” so that the fuzz.partial_ratio is 100. The fuzz.ratio returns the value 74 because it only detects that there is much difference between the two texts.

print(fuzz.ratio('blue shirt','clean blue shirt.')) #74 print(fuzz.partial_ratio('blue shirt','clean blue shirt.')) #100

3. Token_Sort_Ratio – can detect if a text is a part of another text although they are in a different order. Fuzz.token_sort_ratio returns 100 for the text “clean hat and blue shirt” and “blue shirt and clean hat” because they actually mean the same thing, but are in reverse order.

print(fuzz.ratio('clean hat and blue shirt','blue shirt and clean hat')) #42 print(fuzz.partial_ratio('clean hat and blue shirt','blue shirt and clean hat')) #42 print(fuzz.token_sort_ratio('clean hat and blue shirt','blue shirt and clean hat')) #100

4. Token_Set_Ratio – can detect the text-similarity accounting for the partial text, text order, and different text lengths. It can detect that the text “clean hat” and “blue shirt” is part of the text “People want to wear a blue shirt and clean hat” in a different order. In this study, we only use “Token_Set_Ratio” as it is the most suitable.

print(fuzz.ratio('clean hat and blue shirt','People want to wear blue shirt and clean hat')) #53 print(fuzz.partial_ratio('clean hat and blue shirt','People want to wear blue shirt and clean hat')) #62 print(fuzz.token_sort_ratio('clean hat and blue shirt','People want to wear blue shirt and clean hat')) #71 print(fuzz.token_set_ratio('clean hat and blue shirt','People want to wear blue shirt and clean hat')) #100

The following cell will load the training dataset and add features of hash as well as token set ratio.

# load training set trainingSet = pd.read_csv('D:/new_training_set.csv', index_col=0).reset_index() # Compute imagehash difference hashDiff = [] for i in trainingSet.index: hash1 = imagehash.average_hash(Image.open(path_img + trainingSet.iloc[i,2])) hash2 = imagehash.average_hash(Image.open(path_img + trainingSet.iloc[i,4])) diff = hash1 - hash2 hashDiff.append(diff) trainingSet = trainingSet.iloc[:-1,:] trainingSet['hash'] = hashDiff # Compute token_set_ratio Token_tes = [] for i in trainingSet.index: TokenSet = fuzz.token_set_ratio(trainingSet.iloc[i,1], trainingSet.iloc[i,3]) TokenSet = (i, TokenSet) Token_tes.append(TokenSet) dfToken = pd.DataFrame(Token_tes) trainingSet['Token'] = dfToken

Below is the illustration of the training dataset. It is actually not the original dataset because the original dataset is not in the English language. I create another data in English for understanding. Each row has two products. The columns “text_1” and “image_1” belong to the first product. The columns “text_2” and “image_2” belong to the second product. “Label” defines whether the pairing products are the same (1) or not (0). Notice that there are other two columns: “hash” and “tokenSet”. These two columns are generated, not from the original dataset, but from the above code.

index text_1 image_1 text_2 image_2 Label hash tokenSet

0 Blue shirt Gdsfdfs.jpg Blue shirt. Safsfs.jpg 1 6 100

1 Clean hat Fsdfsa.jpg Clean trousers Yjdgfbs.jpg 0 25 71

2 mouse Dfsdfasd.jpg mouse Fgasfdg.jpg 0 30 100

. . . . . . . . . . . . . . . . . . . . . . . .

Applying Machine Learning

Now, we know that lower Imagehash difference and higher Token_Set_Ratio indicates that a pair of products are more likely to be the same. The lowest value of imagehash is 0 and the highest value of Token_Set_Ratio is 100. But, the question is how much the thresholds are. To set the thresholds, we can use the Decision Tree Classifier.

A Machine Learning of Decision Tree model is created using the training dataset. The Machine Learning algorithm will find the pattern of imagehash difference and the token set ratio of identical and different products. The Decision Tree is visualized for the cover image of this article. The code below builds a Decision Tree model with Python. (But, the visualization for the cover image is the Decision Tree generated using R because, in my opinion, R visualizes Decision Tree more nicely). Then, it will predict the training dataset again. Finally, we can get the accuracy.

# Create decision tree classifier: hash and token set Dtc = DecisionTreeClassifier(max_depth=4) Dtc = Dtc.fit(trainingSet.loc[:,['hash', 'tokenSet']], trainingSet.loc[:,'Label']) Prediction2 = Dtc.predict(trainingSet.loc[:,['hash', 'tokenSet']]) metrics.accuracy_score(trainingSet.loc[:,'Label'], Prediction2)

The Decision Tree is used to predict the classification of the training dataset again. The accuracy is 0.728. In other words, 72.8% of the training dataset is predicted correctly.

From the Decision Tree, we can extract the information that if the Imagehash difference is smaller than 12, the pair of products are categorized to be identical. If the Imagehash difference is bigger than or equal to 12, we need to check the Token_Set_Ratio value. The Token_Set_Ratio lower than 97 confirms that the pair of products are different. If else, check whether the Imagehash difference value again. If the imagehash difference is bigger than or equal to 22, then the products are identical. Otherwise, the products are different.

Apply to test dataset

Now, we will load the test dataset, generate the Imagehash difference and Token_Set_Ratio, and finally predict whether each product pair matches.

# path to image path_img = 'D:/test_img/' # load test set test = pd.read_csv('D:/new_test_set.csv', index_col=0).reset_index() # hashDiff list hashDiff = [] # Compute image difference for i in test.index[:100]: hash1 = imagehash.average_hash(Image.open(path_img + test.iloc[i,2])) hash2 = imagehash.average_hash(Image.open(path_img + test.iloc[i,4])) diff = hash1 - hash2 hashDiff.append(diff) test['hash'] = hashDiff # Token_set list Token_set = [] # Compute text difference using token set for i in test.index: TokenSet = fuzz.token_set_ratio(test.iloc[i,1], test.iloc[i,3]) Token_set.append(TokenSet) test['token'] = Token_set

After computing the Imagehash difference and Token_Set_ratio, the next thing to do is to apply the Decision Tree for the product match detection.

# Detecting product match test['labelPredict'] = np.where(test['hash']<12, 1, np.where(test['token']<97, 0, # or test['labelPredict'] = Dtc.predict(test[['hash','token']])

index text_1 image_1 text_2 image_2 hash tokenSet labelPredict

0 pen Fdfgsdfhg.jpg ballpoint Adxsea.jpg 8 33 1

1 harddisk Sgytueyuyt.jpg a nice Harddisk Erewbva.jpg 20 100 1

2 eraser stationary Safdfgs.jpg 25 25 0

. . . . . . . . . . . . . . . . . . . . . . . .

The above table is the illustration of the final result. The focus of this article is to demonstrate how to predict whether two images and two texts are similar or the same. You may find out that the Machine Learning model used is quite simple and there is no hyperparameter-tuning or training and test data splitting. Applying other Machine Learning, such as tree-based ensemble methods, can increase the accuracy. But it is not our discussion focus here. If you are interested to learn other tree-based Machine Learning more accurate than Decision Tree, please find an article here.

About Author

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

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Separate Text In Excel (Examples)

Separate text in Excel (Table of Contents)

Introduction to Separate Text in Excel

We sometimes encounter situations where all the data is clubbed into one column, with each segregation in the data marked by some kind of delimiter such as –

Comma – “,”

Semicolon – “;”

Space – “”

Tab – “

Some other symbol

We could also have all the data in a single column with a fixed number of characters marking the segregation in the data.

When data is received or arranged in any of the formats shown above, it becomes difficult to work with because it is not formatted into a proper row and column format. But if we see carefully, in the first screenshot, the columns (as they should be) are separated by semicolons – “;” i.e., for the first row, the first column is the “First Name”, the second column is “Last Name”, and the third column is “Age”. Semicolons separate all the columns. This holds for the rest of the rows. Therefore, we can split the data into a proper row and column format based on the strategic delimiters in the data. Similarly, the second screenshot shows that all the data has been clubbed into a single column. However, upon closer observation, we see that the columns (as they should be) can be differentiated based on their lengths.

The first column is “Name”, followed by “Sales”. We see that the length of “Name” is 4 and the length of “Sales” is 5. This holds true for all the rows in the table. Therefore, we can separate text data in Excel into columns based on their Fixed Lengths. With Excel, we have a solution to these kinds of problems. Two very useful features of Excel are the “Text to Columns” or the “Split Cell”, which helps to resolve these kinds of formatting issues by enabling data re-arrangement or data manipulation/cleaning since it becomes really difficult to work with a lot of all the data in a single column.

Note: Several complicated formulae can also achieve similar results but tend to be very convoluted and confusing. Text to Column is also much faster.

What is Text to Columns?

Typically, when we get the data from databases, CSV, or text sources, we encounter situations as shown above. We have a very handy feature in Excel called “Text to Columns” to resolve these kinds of problems.

It can be found in the Data tab and the “Data Tools” section.

The shortcut from the keyboard is Alt+A+E. This will also open up the “Text to Columns” feature. Let us see some examples to understand how “Text to Columns” will solve our problem.

Examples of Separate text in Excel

Below are the different examples of separating text in Excel:

Example #1

Split First Name, Last Name, and Age into separate text columns in Excel (using delimiters) :

You can download this Separate text Excel Template here – Separate text Excel Template

Let us consider a situation where we have received the data in the following format.

We have “First Name”, “Last Name”, and “Age” data all clubbed into one column. Our objective is to split the data into separate text columns in Excel.

To split the data into separate text columns in Excel, we need to follow the following steps:

Step1 – We will first select the data column:

This will open up the “Text to Columns” wizard.

Step 4 – After this, in the next tab, deselect “Tab” first.

Then select “Semicolon” as the delimiter.

Step 5 – Next, we shall look at the section describing the column data format. We can choose to keep the data as either :

“General” – This converts numeric values to numbers, date values to dates, and remaining as text.

“Text” – Converts all the values to text format.

“Date” – Converts all the values to Date format (MDY, DMY, YMD, DYM, MYD, YDM)

Ignore Column – This will skip reading the column.

Next, we shall look at the “Advanced” option.

“Advanced” allows us to choose the decimal separator and the thousands separator.

Next, we shall select the destination cell. Now, if we do not modify this, then it will overwrite the original column with “First Name”, the adjacent cell will become “Last Name”, and the cell adjacent to that will become “Age”. If we keep the original column, we must mention a value here (the next adjacent cell).

Our result will be as follows:

Example #2

Split Name and Sales into separate text columns in Excel (using Fixed Width):

Suppose we have a scenario where we have data, as shown below.

As we can see, the entire data has been clubbed into one column (A). But here, we see that the data format is a bit different. We can make out that the first column (as it should be) is “Name,” and the next column is “Sales”. “Name” has a length of 4, and “Sales” has a length of 5. Interestingly, all the names in the rows below also have a length of 4, and all the sales numbers have a length of 5. We can split the data from one column to multiple columns using “Fixed Width” since we do not have any delimiters here.

Step 1 – Select the column where we have the clubbed data.

This will open up the “Text to Columns” wizard.

Step 4 – In the next screen, we shall have to adjust the fixed-width vertical divider lines (called Break Lines) in the Data Preview section.

This can be adjusted as per user requirements.

Step 5 – Next, we shall look at the section describing the column data format. We can choose to keep the data as either –

“General” – This converts numeric values to numbers, date values to dates, and remaining as text.

“Text” – Converts all the values to text format.

“Date” – Converts all the values to Date format (MDY, DMY, YMD, DYM, MYD, YDM)

Ignore Column – This will skip reading the column.

Next, we shall look at the “Advanced” option.

“Advanced” allows us to choose the decimal separator and the thousands separator.

Next, we shall select the destination cell. If we do not modify this, it will overwrite the original column with “Name”; the adjacent cell will become “Sales”. If we keep the original column, we must mention a value here (the next adjacent cell).

Our result will be as follows:

We can also use the same logic to extract the first “n” characters from a data column.

Things to Remember about Separate Text in Excel

We should stop using complicated formulae, copy-paste to split a column (separate the clubbed data from a column), and start using Text to Columns.

Excel will split the data based on the character length in the Fixed-Width method.

In the Delimited method, Excel will split the data based on a set of delimiters such as commas, semicolons, tab,s, etc.

Easily access Text to Columns by using the Keyboard shortcut – Alt+A+E.

Recommended Articles

This has been a guide to Separate text in Excel. Here we discuss the Separate text in Excel and how to use the Separate text in Excel, along with practical examples and a downloadable Excel template. You can also go through our other suggested articles –

Search For Text In Excel

Searching For Text in Excel

In this article, we will learn about Search For Text in Excel. you might have seen situations where you want to extract the text present at a specific position in an entire string using text formulae such as LEFT, RIGHT, MID, etc. You can also combine SEARCH and FIND functions to find the text substring from a given string. However, when you are not interested in finding out the substring but want to find out whether a particular string is present in a given cell, not all of these formulae will work. In this article, we will go through some of the formulae and/or functions in Excel, allowing you to check whether or not a particular string is present in a given cell.

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How to Search Text in Excel?

Search For Text in Excel is very simple and easy. Let’s understand how to Search Text in Excel with some examples.

You can download this Search For Text Excel Template here – Search For Text Excel Template

Example #1 – Using the Find Function

Let’s use the FIND function to find whether a specific string is present in a cell. Suppose you have data as shown below.

As we try to find whether a specific text is present in a given string, we have a function called FIND to deal with it on an initial level. This function returns a position of a substring in a text cell. Therefore, we can say that if the FIND function returns any numeric value, then the substring is present in the text; else, not.

Step 1: In cell B1, start typing =FIND; you can access the function itself.

Step 2: The FIND function needs at least two arguments: the string you want to search and the cell within which you want to search. Let’s use “Excel” as the first argument for the FIND function, which specifies find_text from the formula.

Step 3: We want to find whether “Excel” is present in cell A2 under a given worksheet. Therefore, choose A2 as the next argument to the FIND function.

We are going to ignore the start_num argument as it is an optional argument.

Step 4: Close the parentheses to complete the formula and press Enter Key.

As you can see, this function returned the position where the word “Excel” is present in the current cell (i.e., cell A2).

Step 5: Drag the formula to see the position where Excel belongs under cells A3 and A4.

You can see in the screenshot above that the mentioned string is present in two cells (A2 and A3). In cell B3, the word is not present; hence, the formula provides the #VALUE! Error. This, however, doesn’t always provide a clearer picture. Someone might not be good enough to understand that 1 appearing in cell B2 is nothing but the position of the word “Excel” in the string occupied in cell A2.

After using the above formula, the output is shown below.

Step 7: Drag the formula from cell B2 to cell B4.

Now, we have used IF and FIND in combinations; the cell with no string still gives #VALUE! Error. Let’s try to remove this error with the help of the ISNUMBER function.

ISNUMBER function checks if the output is a number or not. When the output is a number, it will give TRUE as a value; if not a number, then it will give FALSE as a value. If we use this function in combination with IF and FIND, IF functions will give the output based on the values (either TRUE or FALSE) provided by the ISNUMBER function.

Step 8: Use ISNUMBER in the formula above in steps 6 and 7. Press Enter Key after editing the formula under cell B2.

Step 9: Drag the formula across cell B2 to B4.

Example #2 – Using the SEARCH Function

Like the FIND function, the SEARCH function in Excel also allows you to search whether the given substring is present within a text. You can use it on the same lines; we have used the FIND function and its combination with IF and ISNUMBER.

The SEARCH function also searches the specific string inside the given text and returns the text’s position.

I will show you the final formula for finding if a string is present in Excel using the SEARCH, IF, and ISNUMBER functions. You can follow steps 1 to 9, all in the same sequence from the previous example. The only change will be to replace FIND with the SEARCH function.

Use the following formula in cell B2 of the sheet “Example 2”, and press Enter Key to see the output (We have the same data as used in the previous example) =IF(ISNUMBER(SEARCH(“Excel”, A1)), “Text Present”, “Text Not Present”) Once you press Enter Key, you’ll see an output the same as in the previous example.

Drag the formula across cell B2 to B4 to see the final output.

In cells A2 and A4, the word “Excel” is present; hence it has output as “Text Present”. However, in cell A3, the word “Excel” is not present; therefore, it has output as “Text Not Present”.

This is from this article. Let’s wrap the things with some things to be remembered.

Things to Remember About Search For Text in Excel

These functions check whether the given string is present in the text provided. If you need to extract the substring from any string, use the LEFT, RIGHT, and MID functions.

ISNUMBER function is combined so that you are not getting any #VALUE! Error if the string is not present in the text provided.

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This is a guide to Search For Text in Excel. Here we discuss How to Search Text in Excel, practical examples, and a downloadable Excel template. You can also go through our other suggested articles –

10 Best Text Games On Android

These text quests are so much fun to kill some free time and be the decider of what happens next in the game. Try out the best text games that will definitely not leave you disappointed.

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Forbidden Valley – Text Adventure

The Forbidden Valley features a fascinating story that can be coupled with over 11 music tracks to add more crisp to it. The best part is the logical development of events that results from random players actions.

The storyline is good enough and is inclusive of mini-games that are so much fun to explore. You are bound to get addicted if you are a fan of reading. Background music teamed with the artworks is bound to stimulate your imagination and take you to another world. The best part is that you can make your way to the end of the game for free.

Download: Forbidden Valley

Sorcery! 3

Download: Sorcery! 3

Kai Chronicles

This one is a killer application that gives you access to twelve gamebooks by Project Aon all at one place. Simply download the gamebook you like and begin the text game fun. You can save the game at any book section and even export or import your saved games to other devices.

Speaking of the gaming experience, all the twelve gamebooks are compelling where you are the decider of your final destiny. Unfortunately, there are no checkpoints in the game that means you will have to start from the beginning of the chapter if you die. There are no wrong answers to any question, just the consequences that impact the final result of your game.

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Choice of Robots

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The storyline is quite engaging with the potential outcome being dependent on the choices you make in the game. Although, the game feels to be rushed in the beginning as you need to decide your relationship with the character as soon as you are introduced to them.

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Medieval Fantasy RPG (Choices Game)

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DEAD CITY Text Adventure & Cyoa

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Paladins: Text Adventure RPG

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Duels RPG – Text Adventure

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Sorcery! 2

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Algorithm-1

Step 1 − Import Text, View, StyleSheet, and Button from ‘react-native’; Also import Speech modules from “expo-speech”.

Step 2 − Make the chúng tôi and write the code.

Step 3 − Specify the text that is to be converted.

Step 4 − Write separate functions to use Speech.speak() methods with different values for rate and pitch. Call these functions using the onPress() of buttons.

Step 5 − Press the buttons and check the results.

Example 1: Text To Speech showing the pitch and speed change using expo-speech in Snack. The important file used in the project is

App.js

App.js : This is the main javascript file for this project.

Example import{Component}from'react'; import{Text,View,StyleSheet,Button}from'react-native'; import*asSpeechfrom'expo-speech'; varmyNote='Ihavewrittenthistexttoshowhowcanwewritesomethingandthenchangeittospeech'; exportdefaultclassTextToSpeechNote1extendsComponent{ Speech.speak(myNote); }; Speech.speak(myNote,{ rate:2, }); } Speech.speak(myNote,{ rate:0.25, }); } Speech.speak(myNote,{ pitch:3, }); } render(){ return( ); } } conststyles=StyleSheet.create({ mainSpace:{ flex:1, justifyContent:'center', backgroundColor:'#cc4668', padding:8, }, TextSty:{ color:'white', fontWeight:"bold", fontSize:20, margin:5, alignItems:"center", }, }); Output

The result can be seen online. As the user types in the code, the Web view is selected by default and the result appears instantly.

img: Text To Speech Conversion showing in the Web view in Snack

Algorithm-2

Step 1 − Import Text, View, StyleSheet, TextInput, Button from ‘react-native’; Also import Speech modules from “expo-speech”.

Step 2 − Make the chúng tôi and write the code.

Step 3 − Specify the text that is to be converted through a TextInput.

Step 4 − Write separate functions to use Speech.pause(), Speech.resume() and Speech.stop() methods. Call these functions using the onPress() of separate buttons.

Step 5 − Press the buttons and check the results.

Example 2: Text To Speech showing the pause, resume and stop methods using expo-speech in Snack. The Important file used in the project is

App.js

App.js : This is the main javascript file for this project.

Example import{Component}from'react'; import{Text,View,StyleSheet,Button,TextInput}from'react-native'; import*asSpeechfrom'expo-speech'; varstrErr="FirstWriteSomeTextinInputBox"; exportdefaultclassTextToSpeechNote1extendsComponent{ constructor(){ super(); this.state={ myNote:'' }; } if(this.state.myNote==''){ Speech.speak(strErr); } else { Speech.speak(this.state.myNote); } }; Speech.pause(); } Speech.resume(); } Speech.stop(); } render(){ return( <TextInput multiline={true} style={styles.inputSty} this.setState({myNote:myNote}); }} value={this.state.myNote} this.setState({myNote:''}); ); } } conststyles=StyleSheet.create({ mainSpace:{ flex:1, justifyContent:'center', backgroundColor:'#cc4668', padding:8, }, TextSty:{ color:'white', fontWeight:"bold", fontSize:20, margin:5, alignItems:"center", }, inputSty:{ marginTop:5, width:'90%', alignSelf:'center', height:300, textAlign:'center', borderWidth:3, fontSize:24, }, }); Output

The result can be seen online. As the user types in the code, the Web view is selected by default and the result appears instantly. Here, the Android simulator is used to show the result.

Showing the result using the Android simulator.

In this article, for Text to Speech conversion, the ways to show different methods from expo-speech are presented on Expo Snack. Firs,t, the methods given for changing Text to Speech form and then increasing or decreasing the rate of speech are shown along with the changing of the pitch. In another example, the pause, resume, and stop methods from Speech are shown while allowing the text to be entered by the user.

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