• V.Parameshwarreddy
• S.Ashokkumar
• B.PanduRangaRaju3

Social media monitoring has been growing day by day so analyzing of social data plays an important role in knowing customer behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the attitude of User review on movies. This paper develops a combined dictionary based on social media keywords and online review and also find hidden relationship pattern from these keyword. In recent years, shopping online is becoming more and more popular. When it need to decide whether to purchase a product or not on line, the opinions of others become important. It presents a great opportunity to share our viewpoints for various products purchase. However, people face the information overloading problem. How to mine valuable information from reviews to understand a user’s preferences and make an accurate recommendation is crucial. Traditional recommender systems consider some factors, such as user’s purchase records, product category, and geographic location. In this work, it propose a sentiment-based rating prediction method to improve prediction accuracy in recommender systems. Firstly, it propose a social user sentimental measurement approach and calculate each user’s sentiment on items. Secondly, it not only consider a user’s own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, consider item reputation, which can be inferred by the sentimental distributions of a user set that reflect customers’ comprehensive evaluation. At last, by fusing three factors-user sentiment similarity, interpersonal sentimental influence, and item’s reputation similarity into recommender system to make an accurate rating prediction. It conduct a performance evaluation of the three sentimental factors on a real-world dataset. Experimental results show the sentiment can well characterize user preferences, which help to improve the recommendation performance

e-commerce, product recommender, product demographic, microblogs, recurrent neural networks

"SOCIAL MEDIA INTERACTION FOR DETECTING FAKE REVIEWS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.264-268, April-2020, Available :http://www.jetir.org/papers/JETIR2004037.pdf

"SOCIAL MEDIA INTERACTION FOR DETECTING FAKE REVIEWS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp264-268, April-2020, Available at : http://www.jetir.org/papers/JETIR2004037.pdf