Crowd based Live Emotion Response Decoder using Social Media Platforms- Twitter

Authors : Shravani Sanjyot Shete

Volume/Issue : Volume 7 - 2022, Issue 6 - June

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Here in this project the main goal is to gain access to the social media platform posts, here we use Twitter. The most important asset here is the tweets that users post using their account. Using the Tweepy library in Python we extract these tweets using a get request and perform sentiment analysis on them. This is a classic example of using artificial intelligence for data analysis. Theory understands whether a tweet is categorized into a positive or negative category. The main intention here is to identify the crowd emotion using this method. This solution can be used in various fields and applications to gain a large scale input on an ongoing topic. We are using social media platforms such as it is one of the most used platforms. Using sentimental analysis and hashtags we are able to gauge the sentiment behind the tweet. To analyze the tweets we are using the following libraries, TextBlob - this is used to keep processed the textual data gain from this live data set from Twitter. For the implementation of the project we have gone through various approaches with different types of dataset such as hashtagged dataset, Emoticon dataset, Positive and Negative dataset. After the pre-processing of these datasets we calculate the accuracy and check which among the following gives us the maximum accuracy and we continue to use those methods for the further classification of our extracted features.

Keywords : Artificial Intelligence, Twitter API, Sentimental Analysis, Textblob Library, Data Set, Vader Sentiment, Accuracy.


Paper Submission Last Date
31 - March - 2024

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