sentiment analysis text extraction from social media
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This document outlines a project on text extraction and sentiment analysis from social media. It discusses extracting tweets using APIs, preprocessing the text by removing stop words and noise, extracting features like capitalization and emojis, and classifying the sentiment using algorithms like Naive Bayes. The goal is to build a tool that can measure sentiment polarity accurately. It describes the modules including data collection, tokenization, preprocessing, feature extraction, and classification. Future work includes improving the dictionary and parameters to enhance accuracy and developing mobile applications.
sentiment analysis text extraction from social media
- 1. Text Extraction In Social Media BY:- RAVINDRA CHAUDHARY SACHIN SINGH UNDER THE GUIDENCE OF MRS. SMITA TIWARI
- 2. CONTENT Introduction Problem Statement Objective Methodology Module Future work and scope Conclusion
- 3. INTRODUCTION What is Sentiment Analysis…?? It is the classification of the polarity of given text in the document. The goal is to determine whether the expressed opinion in the text is Positive,Negative or Neutral. For Example:- Positive :- sarvjeet is good guy… negative :- jasleen is misusing the law.. Neutral :- waiting for court decision..
- 4. Problem Statement Sentiment analysis is classifying the polarity of given text in a document in a sentence is positive ,negative or neutral. To collecting data and categorizing into different sets for different purposes. Making sentiment tool for measuring all sentiments by one tool. Increase the accuracy of the result which is measured by sentiment tool.
- 5. Objective To implement naïve baye’s Algorithm for classification to text polarity into Positive , Negative,or Neutral sentiments. Using different type of NLTK classifiers for classifying with more accuracy.
- 6. Methodology 1. DATA COLLECTION download the tweets using Twitter API. 2. TOKENSIER Twitter using POS(part of speech) tagger. 3. PRE-PROCESSING Remove slag(non-english) words Replacing emoticons by their polarity. Remove URL and HASTAG(#),numbers. Peplace sequence of repeted character coooooool by cool. Remove noun and prepositions.
- 7. FEATURE EXTRACTION Percentage of capitalized word No of –ve/+ve capatilized word No of +ve/-ve hastag No of +ve/-ve emoticons No. of negations No. of special characters ex..@#%^* CLASSIFICATION AND PREDECTIONS The model is built to predict the sentiment of new tweets… Feature extracted are next focused to classifier
- 8. MODULES OF PROJECT Module 1:- Extracting data from social media. Module 2:- Tokensing fetched data. Module 3:- Preprocessing fetched data. Module 4:-: feature extraction of data. Module5:- Classification of data.
- 9. MODULE 1:- We are using facepager to extract the data from social media. The following process shows how to fetch data from social media
- 16. Future work and scope Web application can be converted to mobile applications Sentiment analysis may be implemented in futyre for accuracy purposes Updating dictionary for new synonyms and antonyms Data pre-processing using more parameters to get best sentiments
- 17. Conclusion We conclude that using different classifiers it is easier to classify the tweets and documents. By improving the data sets we get more accurate results (sentiments).
- 18. THANKYOU EVERYONE For taking interest in my slides