Filtering Political Sentiment in Social Media from Textual Information

Filtering Political Sentiment in Social Media from Textual Information

Filtering Political Sentiment In Social Media From Textual Information software project report Now a days social media is playing a important role in affecting people’s feelings in side or opposite a government or an institution. Therefore, to understand the sentiment of any posting in social media, an effective procedure is necessity. We have analyzed some Facebook postings to understand political sentiments. In any politically infected posting, there are some dominant words. Student Free Project on Filtering Political Sentiment in Social Media from Textual Information. Click here to get complete Software projects lists.

At beginning, we have created a dictionary consisting of unique words gathered from political or nonpolitical posts or comments and then trained using Naïve Bayes algorithm based on probability algorithm. To identify the sentiment expressed in a new post or comment, we have taken each word of the posting and then compared those with the dictionary words for classification. At last, we have tested our algorithm using two hundred postings from Facebook and our result shows that the method can classify posts or comments with good accuracy.

Conclusion

Filtering Political Sentiment In Social Media From Textual Information software project report Here filtering sentiment, using naïve Bayes is discussed. And the system is capable of filtering the political hazardous sentiments using text mining and web mining techniques. To understand sentiment of any social media post this method can be used. This can be used to create peaceful society by reducing political turbulence. The classification depends on dictionary that we provided and training to the words. Student Free Project on Filtering Political Sentiment in Social Media from Textual Information

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