A Subword-Based Deep Learning Approach for Sentiment Analysis of Political Tweets

Abstract

A Subword-Based Deep Learning Approach for Sentiment Analysis of Political Tweets project report on web mining Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach.

To deal with these challenges, the contribution of this A Subword-Based Deep Learning Approach project report on web mining paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this A Subword-Based Deep Learning Approach project on web mining paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).

Conclusion

In this A Subword-Based Deep Learning Approach for Sentiment Analysis of Political Tweets project report on web mining paper, a SVM and KNN based hybrid model is presented to improve the classification accuracy. A Subword-Based Deep Learning Approach for Sentiment Analysis of Political Tweets The proposed method classified the tweets in positive, negative and neutral sentiments whereas much of the literature in this field is associated with 2-way classification. The work of proposed model has gone through preprocessing stage, features generation stage and classifiers learning stage.

Please use the link below for international payments.

Pay and Download Now