Abstract:
Individuals are using their favorite news sources for getting daily news but ultimately they are unable to get the news of desired interest. Every news website provides its own interface and news order; no one is providing personalized news and focusing on user's interest. Also Pakistan is facing many problems due to terrorism activities inside the country. These activities often make there space on media showing up a bad glimpse of our country though there are many good things/activities happening around us but we see news based on violence and hate speech everywhere on the web Hence, there is need of an efficient and promising ranking algorithm which can process news coming from different sources and combine them on the basis of their semantics i.e. statement is positive or negative and use’s interest from their social media preferences. This study presents methodology for ranking news on the basis of sentiment analysis and user's interest fetched from social media. To do so, we have modeled the relationship between user’s social media preference’s and news; we have extracted categories from social media mapped with general categories of news, this solution also considers sentiment of news. Sentiment analysis can rank the news so that user can look for positive news first to start their day with good mood. To provide promising results this research is carried out to integrate user’s social media preferences and sentiment analysis to build a news recommendation system. Enduring experiments shows that our recommendation system provides positive news and news of user’s interest as it make use of user’s social media profile which is the most updated user profile maintained by user itself.
