Movie Rating and Review Summarization in Mobile Environment

Liu, Chien‐Liang; Hsaio, Wen-Hoar; Lee, Chia-Hoang; Lu, Gen-Chi; Jou, Emery

doi:10.1109/tsmcc.2011.2136334

Cited by 108 publications

(63 citation statements)

References 19 publications

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“…Such user reviews have potential to provide a system with more detailed and consistent user preference information. In other words, user text reviews can be used for generating a rating on Characteristics of a movie such as Directing, Story/Plot, Cinematography, Editing, Acting, Production Design, Sound and many other features, in conjunction with numerical ratings, to generate a more efficient recommendation process [3] Figure 3: Movie Review Flowchart [3] 3. PROPOSED FRAMEWORK…”

Section: Review Based Filtering For Polarity Score Generationmentioning

confidence: 99%

“…Algorithms used in this paper for movie recommendations are content based algorithm [2] , collaborative filtering algorithm [2] and review based text mining algorithm [3] . The content-based algorithm consists of user's information such as their interest, favorites, priorities etc.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, online movie reviews have become a common source for users to share and collect data about movies, but there have been very few studies that accurately integrate text mining techniques with traditional CF approaches to improve the suggestions based on user's preferences. Thus this paper proposes a combined approach of content based algorithm, collaborative filtering and review based algorithm that integrates user text reviews [3] and user numeric ratings [2] in order to model user's preferences better and in turn improve the performance of a movie recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Three Way Hybrid Movie Recommendation Syste

Soni¹,

Goyal²,

Vadera³

et al. 2017

IJCA

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Recommendation Systems or Engines are found in many applications. These systems or Engines offer the user or service subscriber with a list of suggestions or recommendations that they might choose based on the user's already known preferences. In this paper, the focus is on combining a content-based algorithm, a User-based collaborative filtering algorithm, and review based text mining algorithm in the application of a tailored movie recommendation system. Here movies are recommended based on ratings explicitly provided by the user and according to the ratings and reviews of movies provided by other users as well. Here the propose is to generate polarity ratings to Characteristics of a movie instead of generating a wholesome rating to an available text based review to gain better insights about preferences of users, thus refining Movie recommendation systems further.

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Section: Review Based Filtering For Polarity Score Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Three Way Hybrid Movie Recommendation Syste

Soni¹,

Goyal²,

Vadera³

et al. 2017

IJCA

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“…But Model Based approach is more accurate than Lexical based approach. Chien-Liang Liu et al 2012 [11].This paper describes the design to develop Movie-Rating system on mobile environment. Information is rated based upon the Sentiment Analysis result.…”

Section: Fig: Text Mining Process Infrastructuresmentioning

confidence: 99%

Review Paper on Text mining and Sentimental analysis

Sharma¹

2017

ijecs

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Abstract:In this paper we discussed about most emerging field of computer science engineering name as Big Data. This paper provides you detailed information about big data from basic to advance level. In this paper we presented detailed review of the sentimental analysis and text analysis which will provide you deep insight of the work done in this and provides outline about the scope in this area and motivate research work in this and apply it on the real life application. It is solitary of the majority vigorous research areas in natural language processing and text mining in latest years. Its attractiveness is primarily due to two reasons. Due to extensive assortment of applications because opinions are innermost to approximately all human activities and are key influencers of our behaviors. Whenever we necessitate making a pronouncement, we want to hear others" opinions it presents many challenging research problems, which had never been attempted before the year 2000. Part of the reason for the lack of study before was that there was little opinionated text in digital forms

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“…The following few works are related to this technique. Liu et al (2012), proposed designed and developed a movie-rating and review-summarization system in a mobile environment. They used a sentiment classification approach based on Latent Semantic Analysis (LSA) to identify product features.…”

Section: Related Workmentioning

confidence: 99%

An Agent Based Framework for Sentiment Classification of Online Reviews Using Ontology

Kalaivani¹,

Shunmuganathan²

2014

Journal of Computer Science

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In this study, we design and develop an agent based framework for sentiment classification of online reviews using ontology. The book review ranking is based on the sentiment classification result. We propose a novel approach with the help of the JADE platform to solve problems by non-visual automatic sentiment classification. The description of book reviews ranking are generated from the ontology based mapping. This approach employs the data extraction agent which is used to retrieve the books comments i.e., the user reviews from the specified blogs. The Second agent is the recommendation agent i.e., domain ontology is used for identifying domain related features in comments. The Third agent is feature selection agent in which XML document content is split into single sentence. Each word in the sentence is mapped with ontology. A Mapping process is used for identifying the domain related sentences in that context. These processes are used for ranking the book results based on customer reviews. The book review ranking system can be extended to other product-review easily.

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Movie Rating and Review Summarization in Mobile Environment

Cited by 108 publications

References 19 publications

A Three Way Hybrid Movie Recommendation Syste

A Three Way Hybrid Movie Recommendation Syste

Review Paper on Text mining and Sentimental analysis

An Agent Based Framework for Sentiment Classification of Online Reviews Using Ontology

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