Emotion Analysis of any text | Twitter data extraction and sentiment analysis based on COVID data
In this application, we have dashboard where all the information about our service will be given. Initially user gets 4 services which are:-
- Extract Tweets
- Sentiment Analysis based on COVID Model
- Use case of Google Perspective API
- Extract tweet with sentiment score Main aim is to provide easy data mining as well as basic sentiment analysis using different model as well as API.
Nowadays, sentiment analysis or opinion mining is a hot topic in machine learning. We are still far to detect the sentiments of corpus of texts very accurately because of the complexity in the English language. In this project we tried to show the basic way of classifying tweets into positive or negative category using LSTM as baseline and how language models are related to the LSTM and can produce better results. We could further improve our classifier by trying to extract more features from the tweets, trying different kinds of features, tuning the parameters of the LSTM, TextBlob, perspective API classifier, or trying another classifier all together. As we know every coin have two sides, sentiment analysis is great but it’s a difficult task. The difficulty increases with increase in complexity of opinions expressed. In some of the fields like product reviews, face recognition, span filter etc. are relatively easy whereas fields like books, movies, art, music, indirect expressions of opinion are more difficult. Sentiment analysis is in demand because of its efficiency. Thousands of text documents can be processed for sentiment in seconds, compared to the hours by a team of people to manually complete it. It is so efficient, accurate and fast that many businesses are adopting text and sentiment analysis and incorporating it into their business processes.