Sentiment analysis of Twitter data using python
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The document presents a project on sentiment analysis of Twitter data, highlighting the growing relevance of Twitter as a source of public opinion with over 500 million tweets daily. It outlines the automation of tweet extraction and classification into positive or negative sentiments, utilizing various Python libraries such as Tweepy, TextBlob, and NLTK. The conclusion emphasizes the successful development of a web-based analysis system while also noting the challenges encountered during the project.
Sentiment analysis of Twitter data using python
- 1. PRESENTATION ON: SENTIMENT ANALYSIS OF TWITTER DATA
- 2. Introduction With the huge amount of increase in the web technologies, the no of people expressing their views and the opinion via web are increasing. This information is useful for everyone like businesses, governments and individuals with 500+ million tweets per day , twitter is becoming a major source of information. Input to our model is the raw data extracted from tweets. For the same, we automate the process of tweet extraction and categorizing it into two categories i.e. positive or negative. The content in twitter generated by the user is about different kinds of products, event, people and political affairs. Performing sentiment analysis on tweets is considered best due to the following reasons: 1. Tweets are abstract in nature. 2. Analysis in real time can be done. 3. A vast variety of tweets for performing the analysis.
- 3. Technology PlatformAnalysis Platform : Python Web development framework : Django Developing language : Python
- 4. Tweepy Tweepy is open-sourced and enables Python to communicate with Twitter platform and use its API. It is twitter library in python. TextBlob TextBlob is a Python (2 and 3) library for processing textual data. 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. NLTK The Natural Language Toolkit (NLTK) is a platform used for building Python programs that work with human language datat. It contains text processing libraries for tokenization, parsing, classification, stemming, tagging and semantic reasoning. Chicksexer It is a python package. It receives the string of a person name and returns probability estimate of its gender.
- 5. Reason for Choosing Python Python is open source. All development tools for python is free. Development is possible most OS platform. Huge resources available
- 6. Application Snapshots This chapter highlights the results of the project & the snapshots for each of the activities are shown along with the discussion of each activity describing its working. Each snapshot describes every single step of the “Sentiment Analysis OF Twitter Data”.
- 7. Home Screen
- 8. About Us
- 14. Contact Us
- 15. Learning from the project Python development fundamental. Familiarity with Django Framework. Python programming Language basic Use of xml for layout definition Use of mysqlite for database management in Different technique of debugging web application
- 16. Conclusion The project exposed us to the latest technology in the area of analysis of twitter data. Thus, this project successfully demonstrated a web based “analysis system” where tweets are used as analysis medium. We faced many challenges and problem during the development time. It was a nice learning curve to work on python development.