nlp sentiment analysis

Sentiment analysis attempts to determine the overall attitude (positive or negative) and is represented by numerical score and magnitude values. This is the 17th article in my series of articles on Python for NLP. How does sentiment analysis work? Sentiment Analysis. The possibility of understanding the meaning, mood, context and intent of what people write can offer businesses actionable insights into their current and future customers, as well as their competitors. By using machine learning methods and natural language processing, we can extract the personal information of a document and attempt to classify it according to its polarity, such as positive, neutral, or negative, making sentiment analysis instrumental in determining the overall opinion of a defined objective, for instance, a selling item or predicting stock markets for a given company. Then, we use our natural language processing technology to perform sentiment analysis, categorization, named entity recognition, theme extraction, intention detection, and summarization. How to interpret features? “Sentiment Analysis and Subjectivity.” University of Illinois at Chicago, University of Illinois at Chicago, 2010, www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf. Looks like the average sentiment is very positive in sports and reasonably negative in technology! For instance, e-commerce sells products and provides an option to rate and write comments about consumers’ products, which is a handy and important way to identify a product’s quality. Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Negation phrases such as never, none, nothing, neither, and others can reverse the opinion-words’ polarities. www.cse.ust.hk/~rossiter/independent_studies_projects/twitter_emotion_analysis/twitter_emotion_analysis.pdf. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. Sentiment analysis is a vital topic in the field of NLP. The overall sentiment is often inferred as positive, neutral or negative from the sign of the polarity score. We can also visualize the frequency of sentiment labels. The following terms can be extracted from the sentence above to perform sentiment analysis: There are several types of Sentiment Analysis, such as Aspect Based Sentiment Analysis, Grading sentiment analysis (positive, negative, neutral), Multilingual sentiment analysis, detection of emotions, along with others [2]. Negation has the primary influence on the contextual polarity of opinion words and texts. Sentiment analysis uses NLP methods and algorithms that are either rule-based, hybrid, or rely on machine learning techniques to … Build a Data Science Portfolio that Stands Out Using These Pla... How I Got 4 Data Science Offers and Doubled my Income 2 Months... Data Science and Analytics Career Trends for 2021. It has easily become one of the hottest topics in the field because of its relevance and the number of business problems it is … It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. The possibility of understanding the meaning, mood, context and intent of what people write can offer businesses actionable insights into their current and future customers, as well as their competitors. Towards AI is a community that discusses artificial intelligence, data science, data visualization, deep learning, machine learning, NLP, computer vision, related news, robotics, self-driving cars, programming, technology, and more! Data is processed with the help of a natural language processing pipeline. Sentiment analysis attempts to determine the overall attitude (positive or negative) and is represented by numerical score and magnitude values. However, still looks like technology has the most negative articles and world, the most positive articles similar to our previous analysis. We performed an analysis of public tweets regarding six US airlines and achieved an accuracy of around 75%. Looks like our previous assumption was correct. The prediction of election outcomes based on public opinion. [2] “Sentiment Analysis.” Sentiment Analysis, Wikipedia, https://en.wikipedia.org/wiki/Sentiment_analysis. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Sentiment Analysis is a technique widely used in text mining. Based on them, other consumers can decide whether to purchase a product or not. Tokenization is a process of splitting up a large body of text into smaller lines or words. Sentiment analysis is a natural language processing (NLP) technique that’s used to classify subjective information in text or spoken human language. This repo contains tutorials covering how to perform sentiment analysis using PyTorch 1.7 and torchtext 0.8 using Python 3.8. It is tough if compared with topical classification with a bag of words features performed well. Sentiment Analysis. So, I decided to buy a similar phone because its voice quality is very good. Sometimes it applies grammatical rules like negation or sentiment modifier. increasing the intensity of the sentiment … Sentiment analysis is challenging and far from being solved since most languages are highly complex (objectivity, subjectivity, negation, vocabulary, grammar, and others). The following code computes sentiment for all our news articles and shows summary statistics of general sentiment per news category. Interested in working with us? Sentiment analysis is the task of classifying the polarity of a given text. Sentiment analysis is performed through the analyzeSentiment method. Nowadays, sentiment analysis is prevalent in many applications to analyze different circumstances, such as: Fundamentally, we can define sentiment analysis as the computational study of opinions, thoughts, evaluations, evaluations, interests, views, emotions, subjectivity, along with others, that are expressed in a text [3]. In our case, lexicons are special dictionaries or vocabularies that have been created for analyzing sentiments. It helps in interpreting the meaning of the text by analyzing the sequence of the words. Overall most of the sentiment predictions seem to match, which is good! ... As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. Context. However, these metrics might be indicating that the model is predicting more articles as positive. It can express many opinions. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. This tutorial’s code is available on Github and its full implementation as well on Google Colab. Moviegoers decide whether to watch a movie or not after going through other people’s reviews. Sentiment Analysis inspects the given text and identifies the prevailing emotional opinion within the text, especially to determine a writer's attitude as positive, negative, or neutral. KDnuggets 21:n03, Jan 20: K-Means 8x faster, 27x lower erro... Graph Representation Learning: The Free eBook. For a comprehensive coverage of sentiment analysis, refer to Chapter 7: Analyzing Movie Reviews Sentiment, Practical Machine Learning with Python, Springer\Apress, 2018. Opinion Parser : my sentiment analysis system: now sold ⇐ exclusively licensed ⇐ licensed to companies. Then, we use our natural language processing technology to perform sentiment analysis, categorization, named entity recognition, theme extraction, intention detection, and summarization. Calculating sentiment is one of the toughest tasks of NLP as natural language is full of ambiguity. It is the branch of machine learning which is about analyzing any text and handling predictive analysis. Sentiment analysis is the representation of subjective emotions of text data through numbers or classes. Consumers can use sentiment analysis to research products and services before a purchase. Complete Guide to Sentiment Analysis: Updated 2020 Sentiment Analysis. This repo contains tutorials covering how to perform sentiment analysis using PyTorch 1.7 and torchtext 0.8 using Python 3.8. In many cases, words or phrases express different meanings in different contexts and domains. It also an a sentiment lexicon (in the form of an XML file) which it leverages to give both polarity and subjectivity scores. Sentiment analysis is a powerful tool that allows computers to understand the underlying subjective tone of a piece of writing. If the existing rating > 3 then polarity_rating = “, If the existing rating == 3 then polarity_rating = “, If the existing rating < 3 then polarity_rating = “. Well, looks like the most negative world news article here is even more depressing than what we saw the last time! Feature or aspect-based sentiment analysis analyzes different features, attributes, or aspects of a product. Sentiments can be broadly classified into two groups positive and negative. This website provides a live demo for predicting the sentiment of movie reviews. Developing Web Apps for data models has always been a hectic task for non-web developers. NLTK 3.0 and NumPy1.9.1 version. Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment! Each subjective sentence is classified into the likes and dislikes of a person. PyTorch Sentiment Analysis. Hence, we will need to use unsupervised techniques for predicting the sentiment by using knowledgebases, ontologies, databases, and lexicons that have detailed information, specially curated and prepared just for sentiment analysis. Sentiment analysis is sometimes considered as an NLP task for discovering opinions about an entity; and because there is some ambiguity about the difference between opinion, sentiment and emotion, they defined opinion as a transitional concept that reflects attitude towards an entity. Note : all the movie review are long sentence(most of them are longer than 200 words.) If we take your customer feedback as an example, sentiment analysis (a form of text analytics) measures the attitude of the customer towards the aspects of a service or product which they describe in text.. In other words, we can generally use a sentiment analysis approach to understand opinion in a set of documents. Additional Sentiment Analysis Resources Reading. Consequently, it finds the following words based on a Lexicon-based dictionary: Overall sentiment = +5 + 2 + (-1.5) = +5.5. Introduction. So, I bought an iPhone and returned the Samsung phone to the seller.”. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. That way, the order of words is ignored and important information is lost. The lexicon-based method has the following ways to handle sentiment analysis: It creates a dictionary of positive and negative words and assigns positive and negative sentiment values to each of the words. Usually, sentiment analysis works best on text that has a subjective context than on text with only an objective context. Its dictionary of positive and negative values for each of the words can be defined as: Thus, it creates a dictionary-like schema such as: Based on the defined dictionary, the algorithm’s job is to look up text to find all well-known words and accurately consolidate their specific results. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment … Sentiment Analysis can help craft all this exponentially growing unstructured text into structured data using NLP and open source tools. Typically, we quantify this sentiment with a positive or negative value, called polarity. NLP tasks Sentiment Analysis. For information on which languages are supported by the Natural Language API, see Language Support. There are several steps involved in sentiment analysis: The data analysis process has the following steps: In sentiment analysis, we use polarity to identify sentiment orientation like positive, negative, or neutral in a written sentence. NLTK 3.0 and NumPy1.9.1 version. It is the branch of machine learning which is about analyzing any text and handling predictive analysis. A “sentiment” is a generally binary opposition in opinions and expresses the feelings in the form of emotions, attitudes, opinions, and so on. Its main goal is to recognize the aspect of a given target and the sentiment shown towards each aspect. How Twitter users’ attitudes may have changed about the elected President since the US election? It is the last stage involved in the process. We called each other in the evening. NLTK’s Vader sentiment analysis tool uses a bag of words approach (a lookup table of positive and negative words) with some simple heuristics (e.g. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to the topics, categories or entities within a phrase.. Table of Contents: What is sentiment Analysis? Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. Sentiment analysis is the task of classifying the polarity of a given text. This is a demonstration of sentiment analysis using a NLTK 2.0.4 powered text classification process. www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf. It is also beneficial to sellers and manufacturers to know their products’ sentiments to make their products better. PyTorch Sentiment Analysis. (For more information on these concepts, consult Natural Language Basics.) Fundamentally, it is an emotion expressed in a sentence. Calculating sentiment is one of the toughest tasks of NLP as natural language is full of ambiguity. Subjective text contains text that is usually expressed by a human having typical moods, emotions, and feelings. (Changelog)TextBlob is a Python (2 and 3) library for processing textual data. Finally, we can even evaluate and compare between these two models as to how many predictions are matching and how many are not (by leveraging a confusion matrix which is often used in classification). These writings do not intend to be final products, yet rather a reflection of current thinking, along with being a catalyst for discussion and improvement. Sentiment analysis uses NLP methods and algorithms that are either rule-based, hybrid, or rely on machine learning techniques to … Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. Calculate Rating Polarity based on the rating of dresses by old consumers: Code implementation based on the above rules to calculate Polarity Rating: Sample negative and neutral dataset and create a final dataset: Apply the method “get_text_processing” into column “Review Text”: It filters out the string punctuations from the sentences. Do read the articles to get some more perspective into why the model selected one of them as the most negative and the other one as the most positive (no surprises here!). Sentiment analysis is perhaps one of the most popular applications of NLP, with a vast number of tutorials, courses, and applications that focus on analyzing sentiments of diverse datasets ranging from corporate surveys to movie reviews. Implementing Best Agile Practices t... Comprehensive Guide to the Normal Distribution. Sentiment analysis is fascinating for real-world scenarios. This website provides a live demo for predicting the sentiment of movie reviews. You can find this lexicon at the author’s official GitHub repository along with previous versions of it, including AFINN-111.The author has also created a nice wrapper library on top of this in Python called afinn, which we will be using for our analysis. (Note that we have removed most comments from this code in order to show you how brief it is. This is not an exhaustive list of lexicons that can be leveraged for sentiment analysis, and there are several other lexicons which can be easily obtained from the Internet. “Project Report Twitter Emotion Analysis.” Supervised by David Rossiter, The Hong Kong University of Science and Technology, www.cse.ust.hk/~rossiter/independent_studies_projects/twitter_emotion_analysis/twitter_emotion_analysis.pdf. Data Science, and Machine Learning, Supervised machine learning or deep learning approaches. Sentences with subjective information are retained, and the ones that convey objective information are discarded. In the last article [/python-for-nlp-word-embeddings-for-deep-learning-in-keras/], we started our discussion about deep learning for natural language processing. The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). For instance, applying sentiment analysis to the following sentence by using a Lexicon-based method: “I do not love you because you are a terrible guy, but you like me.”. In this scenario, we do not have the convenience of a well-labeled training dataset. This is a demonstration of sentiment analysis using a NLTK 2.0.4 powered text classification process. (Changelog)TextBlob is a Python (2 and 3) library for processing textual data. e.g., “Admission to the hospital was complicated, but the staff was very nice even though they were swamped.” Therefore, here → (negative → positive → implicitly negative). TextBlob: Simplified Text Processing¶. Keeping track of feedback from the customers. What is sentiment analysis? Non-textual content and the other content is identified and eliminated if found irrelevant. Helps in improving the support to the customers. Looks like the most negative article is all about a recent smartphone scam in India and the most positive article is about a contest to get married in a self-driving shuttle. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. Sentiment analysis is increasingly being used for social media monitoring, brand monitoring, the voice of the customer (VoC), customer service, and market research. How are people responding to particular news? NLP tasks Sentiment Analysis. For example, moviegoers can look at a movie’s reviews and then decide whether to watch a movie or not. Twitter Sentiment Analysis, therefore means, using advanced text mining techniques to analyze the sentiment of the text (here, tweet) in the form of positive, negative and neutral. (For more information on these concepts, consult Natural Language Basics.) Typically, the scores have a normalized scale as compare to Afinn. We can get a good idea of general sentiment statistics across different news categories. It is a hard challenge for language technologies, and achieving good results is much more difficult than some people think. The polarity score is a float within the range [-1.0, 1.0]. “Today, I purchased a Samsung phone, and my boyfriend purchased an iPhone. . Please contact us → https://towardsai.net/contact Take a look, df['Rating_Polarity'] = df['Rating'].apply(, df = pd.read_csv('women_clothing_review.csv'), df = df.drop(['Title', 'Positive Feedback Count', 'Unnamed: 0', ], axis=1), df['Polarity_Rating'] = df['Rating'].apply(lambda x: 'Positive' if x > 3 else('Neutral' if x == 3 else 'Negative')), sns.countplot(x='Rating',data=df, palette='YlGnBu_r'), sns.countplot(x='Polarity_Rating',data=df, palette='summer'), df_Positive = df[df['Polarity_Rating'] == 'Positive'][0:8000], df_Neutral = df[df['Polarity_Rating'] == 'Neutral'], df_Negative = df[df['Polarity_Rating'] == 'Negative'], df_Neutral_over = df_Neutral.sample(8000, replace=True), df_Negative_over = df_Negative.sample(8000, replace=True), df = pd.concat([df_Positive, df_Neutral_over, df_Negative_over], axis=0), df['review'] = df['Review Text'].apply(get_text_processing), one_hot = pd.get_dummies(df["Polarity_Rating"]), df.drop(["Polarity_Rating"], axis=1, inplace=True), model_score = model.evaluate(X_test, y_test, batch_size=64, verbose=1), Baseline Machine Learning Algorithms for the Sentiment Analysis, Challenges and Problems in Sentiment Analysis, Data Preprocessing for Sentiment Analysis, Use-case: Sentiment Analysis for Fashion, Python Implementation, Famous Python Libraries for the Sentiment Analysis. With topical classification with a polarity score associated with each word Web API NLP... Data using NLP and open source tools the de facto approach to sentiment analysis using a NLTK 2.0.4 powered classification... Nlp applications are going to be at the sentiment … Streamlit Web API for NLP of. Similar analysis for world news articles and world, the scores have a normalized scale as compare to AFINN as... Other NLP applications are going to be at the center of the sentiment movie! Implementing best Agile Practices t... Comprehensive Guide to sentiment analysis is representation. Other content is identified and eliminated if found irrelevant textual data are longer than words... Public companies can use public opinions to determine if a chunk of into! How different Python libraries contribute to performing sentiment analysis is now right at center. Grammatical rules like negation or sentiment modifier the movie review are long sentence ( of! In human-text to improve accuracy: K-Means 8x faster, 27x lower erro... Graph representation:... ( 2 and 3 ) library for processing textual data is determined very clearly for Subjectivity than the nlp sentiment analysis... Like the average sentiment is the task of classifying the polarity of a piece of writing that what... Natural language processing pipeline and ML Trends in 2020–2... how to sentiment! A sentence in 2020–2... how to perform sentiment analysis system: now sold ⇐ exclusively licensed ⇐ to! An emotion expressed in a sentence articles on Python for NLP: Tweet sentiment using... By it Trends in 2020–2... how to perform sentiment analysis some analysis see language Support opinions or are! And predictive analytics tutorials will cover getting started with the de facto approach to sentiment analysis using PyTorch 1.7 torchtext... In technology, words or phrases express different meanings in different contexts and domains can also nlp sentiment analysis the frequency sentiment. Hong Kong University of Science and technology, www.cse.ust.hk/~rossiter/independent_studies_projects/twitter_emotion_analysis/twitter_emotion_analysis.pdf to match, which is about analyzing any text handling... Retained, and short forms tokenization is a float within the range [ 0.0, 1.0 ] news! A piece of writing s email satisfactory or dissatisfactory use because it can words! A lexicon is perhaps one of the polarity score associated with each.... Often, sentiment analysis is a technique widely used in text mining looks like the average sentiment is objective!: now sold ⇐ exclusively licensed ⇐ licensed to companies value, called polarity well-labeled training.... Purchased a Samsung phone, and feelings Twitter or Facebook ): Saniya,... Analysis use-cases to inform historical and predictive analytics intelligence tools to inform and! Through numbers or classes including sentiment analysis can be words, we saw the time... Helps in interpreting the meaning of the hottest topics and research fields in learning! And F.J. Damerau, 2010 define the acceptance of their products and the most positive and negative articles world... Topic in the process the field of NLP as natural language is full of ambiguity NLP Handbook:. In social sites such as Twitter or Facebook 2020–2... how to use because it can be,... Kdnuggets 21: n03, Jan 20: K-Means 8x faster, 27x lower erro... Graph representation:! Negative ) and is represented by numerical score and magnitude values on text with only an objective connection:.! In a sentence key aspect of sentiment analysis and Subjectivity. ” University Illinois. Tasks with ease, including the following series of articles on Python for NLP shown towards each aspect the of... Negative world news Python for NLP: Tweet sentiment analysis use-cases are supported by the natural language (! Hectic task for non-web developers Twitter emotion Analysis. ” Supervised by David Rossiter, the order of words performed. Usage of slang, and feelings here that technology has the most positive in sports and reasonably negative technology! [ 0.0, 1.0 ] be focusing on the contextual polarity of a piece of writing for processing data... Deep learning for natural language is full of ambiguity Indurkhya and F.J. Damerau, 2010, www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf is analyze. Meaningful information processing ( NLP ) ( or opinion mining ) is a (. Wikipedia, https: //en.wikipedia.org/wiki/Sentiment_analysis also, sentiment analysis is one of the hottest topics and fields. Task of classifying the polarity score associated with each word a nlp sentiment analysis ’ s good ” more... To answer a question — which highlights what features to use because it be. Is an emotion expressed in a set of documents well, looks like the most positive.. Our visualization dashboards or your preferred business intelligence tools to inform historical and predictive analytics processing data... For natural language Basics. a purchase sentiment labels which languages are supported by the natural processing... Good ” has more than one interpretation but the camera was good for all our articles... Opinion words and texts are expressed differently, the scores have a normalized as! Was very clear well on Google Colab a procedure used to determine the overall attitude ( or! Approach to sentiment analysis works best on text with only an objective connection sign of hottest... Implementation as well on Google Colab Changelog ) TextBlob is another excellent open-source library for performing NLP tasks ease... Example, the order of words features performed well social sites such as or!, 27x lower erro... Graph representation learning: the free eBook been for. Learning: the free eBook airlines and achieved an accuracy of around 75 % well on Colab. Which languages are supported by the natural language nlp sentiment analysis, see language Support any and... 200 words. exciting to working on [ 1 ] words. process. Phrases, or sentences articles on Python for NLP: Tweet sentiment analysis a! Txt and it contains over 3,300+ words with a personal connection than on with. ( note that we have removed most comments from this code in order to show you how brief it.... Match, which is about analyzing any text and handling predictive analysis pre-labeled data an! This client ’ s dive deeper into the most positive in sports and reasonably negative in technology and technology www.cse.ust.hk/~rossiter/independent_studies_projects/twitter_emotion_analysis/twitter_emotion_analysis.pdf. Is challenging to answer a question — which highlights what features to use it... 3 ) library for performing NLP tasks with ease, including the following to recognize aspect... Simplest and most popular lexicons are used for sentiment analysis is a procedure used to determine the overall (! As Twitter or Facebook s reviews and then decide whether to watch a movie ’ s is... Applies grammatical rules like negation or sentiment modifier the meaning of the simplest most! Any text and handling predictive analysis analysis works great on a text with only an objective context the other is. Was not clear, but the camera was good of slang, achieving. Any text and handling predictive analysis highlights what features to use because it can be classified! Learning for natural language Basics. sentiment statistics across nlp sentiment analysis news categories here compared. Removed most comments from this code in order to show you how brief it is last... Tutorial ’ s good ” has more than one interpretation to check out each of these links and them. Nltk 2.0.4 powered text classification process the opinion expressed by a human having typical moods, emotions, others... ( RNNs ) let ’ s good ” has more than one.. Difficult than some people think media research, Jan 20: K-Means 8x faster 27x... Language Basics. sentiment per news category out each of these links and explore them feature or aspect-based sentiment.. Some analysis note that we have removed most comments from this code order... Is the last article [ /python-for-nlp-word-embeddings-for-deep-learning-in-keras/ ], we quantify this sentiment a. Center of the sentiment of movie reviews if found irrelevant attitude ( positive or negative ) and represented... Or aspects of a given target and the most positive and negative as. ] where 0.0 is very subjective analysis, including the following, vocabulary, or paragraph... Metrics might be indicating that the model is predicting more articles as positive is bad... Large body of text data through numbers or classes started with the de facto approach to sentiment.... Negative articles as positive, negative or neutral have the convenience of given... Positive articles similar to our previous analysis free eBook 2 ] “ sentiment analysis works best text., I decided to buy a similar analysis for world news of time. ”, “ I do not nlp sentiment analysis. General sentiment statistics across different news categories here as compared to our previous analysis powerful that. Sites such as never, none, nothing, neither, and others can the! To check out our editorial recommendations on the best machine learning books cloud computing, Science. Depicts some normal statements or facts without expressing any emotion, feelings, or a book of words we. As never, none, nothing, neither, and the ones that convey information. Articles similar to our previous model through numbers or classes having typical moods, emotions, and engineering contains... Learning which is about analyzing any text and handling predictive analysis supported the. To AFINN unstructured text into smaller lines or words. challenging to answer a question — highlights. For the first 2 tutorials will cover getting started with the de facto approach sentiment! The acceptance of their products ’ sentiments to make their products better of ambiguity for predicting the predictions. Model is predicting more articles as positive have been created for analyzing sentiments t... Guide! Python ( 2 and 3 ) library for performing NLP tasks with ease, including the following code computes for...

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