A Neural Network-Inspired Approach for Improved and True Movie Recommendations

Abstract

In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

“…In the past, SenticNet has been employed for many different tasks other than polarity detection, e.g., recommendation systems [24], stock market prediction [31], political forecasting [46], irony detection [60], drug effectiveness measurement [42], depression detection [14], mental health triage [1], vaccination behavior detection [27], psychological studies [29], and more. Figure 8: Sentiment data flow for the sentence "The car is very old but rather not expensive" using linguistic patterns.…”

User-Query Processing through Dynamic Tweets Status Recommender System

“…Table 4 presents the Twitter likes of the tweets category from reviews. [16]. The F score results for the proposed recommender system are compared with other parameters and other recommendation systems.…”

“…The second applies to tweets information seekers, in which users post a rare tweet for understanding the tweet content and follow them regularly, and the third is relevant to the relationship of user tweets [15] like friends, relatives, in which the personal tweets are retweet content. The above categories are ranked based on the users, followers, and similar results among the tweet and retweet [16]. The ranking is made on several users, several followers, and the retweets with the recommended information sources and seekers.…”

Knowledge Extraction from Open Data Repository

“…The rating is for multiple users, supporters, and retweets with the suggested providers and searchers of knowledge. Depending on these ordering, users and followers get the reputation of relatively large text containing signs of impact in the production of tweets [37]. Posts and repost individuals are classified first, social media posts and tweets are the subsequent most important for tweet users, and the third reciprocate relation to the discussion forums of these ratings [38].…”