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

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          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.

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          Utilizing social media data for pharmacovigilance: A review.

          Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media.
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            Social media competitive analysis and text mining: A case study in the pizza industry

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              Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                CIN
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2019
                4 August 2019
                : 2019
                : 4589060
                Affiliations
                1Department of Computer Science & IT, Islamia University of Bahawalpur, Bahawalpur, Pakistan
                2Faculty of Information and Communication Technologies, BUITEMS, Quetta, Pakistan
                Author notes

                Guest Editor: Ricardo Soto

                Author information
                https://orcid.org/0000-0002-5161-6441
                Article
                10.1155/2019/4589060
                6701398
                4a336a72-c334-489f-8acc-6676fee43d70
                Copyright © 2019 Muhammad Ibrahim et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 April 2019
                : 28 May 2019
                : 17 June 2019
                Categories
                Research Article

                Neurosciences
                Neurosciences

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