MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

Abstract

Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.

Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

“…The same idea was adopted in [43], [44]. In [45], the authors presented a reputation system dedicated to movies and TV shows. The model integrates fine-grained opinion mining (Multinomial Naïve Bayes classifier trained on the SST-5 dataset [46]) and semantic analysis (Embeddings from Language Models (ELMo) [47]) to generate a realistic reputation value from user's reviews.…”

Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

“…Experimental results using several real-world datasets of different domains collected from IMDb, TripAdvisor, and Amazon websites confirm the effectiveness of the proposed method in generating and visualizing reputation compared to the state-ofthe-art reputation systems. Authors in [8] proposed a new reputation generation system that incorporates fine-grained opinion mining and semantic analysis to generate reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of document-level sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis where reviews are classified into five classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive.…”

Stance Analysis of Policies Related to Emission Test Obligations using Twitter Social Media Data

“…Corporate communication facilities, customer service, and product campaigns are conducted via Twitter. Research topics are also very broad, such as politics [5], education [6], and film [7].…”