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Fast and accurate sentiment classification us and naive bayes model b516001
- 1. Fast and Accurate Sentiment Classification Using NLTK And Naive Bayes Model Presented By - Abhisek Sahoo (ID - B516001) Computer Engineering(2016-20) Under the guidance of Prof. Sabyasachi Patra oj Department of Computer Science And Engineering International Institute of Information Technology, Bhubaneswar
- 2. Sentiment Analysis ? Aim to determine the attitude of a speaker/writer with respect to some text
- 3. Positive or Negative ? This is a good book ! Positive This is a good book ! I like it ! Positive (more) This is a bad book! Negative The first chapter is good, But the rest is terrible. Negative
- 4. Features of Sentiment Analysis ➢ Identifying trends of public opinion in social media ➢ Marketing and consumer research ➢ Customer feedback on new product launches, political campaigns ➢ To facilitate smarter business decisions ➢ Enhanced product recommendation
- 5. How to compute it ?
- 7. Natural Language Processing ➢ Field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. ➢ Ability to draw insights from data contained in emails, videos, and other unstructured material. ➢ The various aspects of NLP include Parsing, Machine Translation, Language Modelling, Machine Learning, Semantic Analysis etc.
- 8. Natural Language Toolkit ➢ NLTK is a leading platform for building Python programs to work with human language data. ➢ It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet. ➢ It provides a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
- 9. Dataset : NLTK Corpora ➢ A publicly available dataset of tweets from the Natural Language Toolkit Corpus Library is used. ➢ Tweets dataset comprised of a ample collection of individual emotions and captures most of the adjectives important to sentiment classification. ➢ It consists of 30,000 extremely polar tweets for training dataset and 10,000 for testing dataset. ➢ Both the training and test sets comprises of an identical number of negative and positive tweets.
- 11. Tokenization The process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens.
- 12. Normalization ➢ Normalization in NLP is the process of converting a word to its canonical form. ➢ Stemming is a process of removing affixes from a word. ➢ Lemmatization is a process in which a word normalizes with the context of vocabulary and morphological analysis of words in text. ➢ Wordnet is a lexical database available in NLTK for the English language that helps the script determine the base word.
- 13. Removal of Noise/Stop Words ➢ Noise is any part of the text that does not add any meaning to data. ➢ It is necessary to remove all hyperlinks, @ symbol of tweeter handles ,punctuation and special characters. ➢ Also required to remove the stop words like “is”, “a” and “the” from the sentence.
- 14. Determine Word Density ➢ The most basic form of analysis on textual data is to take out the word frequency. ➢ After compiling all words in the sample of tweets, the most common words can be found out using the FreqDist class of NLTK.
- 15. Separate The Data Training data to train the model Testing data to compute the accuracy
- 16. Training Data (With Labels) Model Real Data Trained Model Prediction
- 17. Train The Data
- 18. Compute The Accuracy Accuracy = No. of correct predictions / Total no. of statements
- 19. Result
- 20. Future Work A web based application can implemented for better access Web scraping can be implemented for direct retrieval of tweets or reviews from Twitter or any other platform More classification categories can be added to determine the sentiment more specifically Supervised with multiple languages to make it more local
- 21. Conclusion We conclude that by using various NLTK modules for preprocessing and NLTK Naive Bayes classifier it is easier to classify the tweets and get better accuracy.
- 22. References ➢ https://www.researchgate.net/publication/220482883_NLTK_the_Natural_Language_ Toolkit ➢ Basic Sentiment Analysis using NLTK - Towards Data Science ➢ https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/ ➢ https://www.kaggle.com/lakshmi25npathi/sentiment-analysis-of-imdb-movie-reviews ➢ http://ijcsit.com/docs/Volume%206/vol6issue06/ijcsit20150606134.pdf ➢ https://pdfs.semanticscholar.org/c151/dfad8c1bf88b0afc716758c77d533ded7dd0.pdf
- 23. Thank You !
Editor's Notes
- #9: NLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning.
- #10: I choose tweets as my dataset since it comprise of a ample collection of individual emotions and captures most of the adjectives important to sentiment classification.