Effective Comment Sentence Recognition for Feature-Based Opinion Mining

An Overview of Feature Based Opinion Mining

Advances in Intelligent Systems and Computing - Intelligent Systems Technologies and Applications 2016 ◽

10.1007/978-3-319-47952-1_51 ◽

2016 ◽

pp. 633-645 ◽

Cited By ~ 1

Author(s):

Avinash Golande ◽

Reeta Kamble ◽

Sandhya Waghere

Keyword(s):

Opinion Mining ◽

Feature Based

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Application of Sentiment Analysis in Movie reviews

Advances in Business Information Systems and Analytics - Sentiment Analysis and Knowledge Discovery in Contemporary Business ◽

10.4018/978-1-5225-4999-4.ch006 ◽

2019 ◽

pp. 77-90 ◽

Cited By ~ 5

Author(s):

ThippaReddy Gadekallu ◽

Akshat Soni ◽

Deeptanu Sarkar ◽

Lakshmanna Kuruva

Keyword(s):

Sentiment Analysis ◽

Experimental Work ◽

Opinion Mining ◽

Rating Systems ◽

Domain Specific ◽

Analysis Process ◽

The People ◽

Textual Data ◽

Feature Based ◽

The Web

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured, or unstructured textual data. In this chapter, the authors try to focus the task of sentiment analysis on IMDB movie review database. This chapter presents the experimental work on a new kind of domain-specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. The authors have devised an aspect-oriented scheme that analyzes the textual reviews of a movie and assign it a sentiment label on each aspect. Finally, the authors conclude that incorporating syntactical information in the models is vital to the sentiment analysis process. The authors also conclude that the proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain.

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Feature Based Opinion Mining for Hotel Profiling

Communications in Computer and Information Science - Artificial Intelligence ◽

10.1007/978-981-13-9129-3_16 ◽

2019 ◽

pp. 219-231

Author(s):

Dilum Gunathilaka ◽

Shamila Pathirana ◽

Sasanka Senarathne ◽

Jithmi Weerasekara ◽

Thushari Silva

Keyword(s):

Opinion Mining ◽

Feature Based

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Research on feature-based opinion mining using topic maps

The Electronic Library ◽

10.1108/el-11-2014-0197 ◽

2016 ◽

Vol 34 (3) ◽

pp. 435-456 ◽

Cited By ~ 2

Author(s):

Lixin Xia ◽

Zhongyi Wang ◽

Chen Chen ◽

Shanshan Zhai

Keyword(s):

Domain Knowledge ◽

Opinion Mining ◽

Specific Knowledge ◽

Content Type ◽

Topic Maps ◽

Domain Specific ◽

Product Features ◽

Domain Specific Knowledge ◽

Feature Based ◽

Integrate Domain

Purpose Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems. Design/methodology/approach Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words. Findings In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions. Research limitations/implications To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines. Originality/value This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

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Feature-based opinion mining

International Journal Of Data Mining And Emerging Technologies ◽

10.5958/j.2249-3212.1.1.2 ◽

2011 ◽

Vol 1 (1) ◽

pp. 8 ◽

Cited By ~ 1

Author(s):

Priti Srinivas Sajja

Keyword(s):

Opinion Mining ◽

Feature Based

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Feature-based opinion mining through ontologies

Expert Systems with Applications ◽

10.1016/j.eswa.2014.03.022 ◽

2014 ◽

Vol 41 (13) ◽

pp. 5995-6008 ◽

Cited By ~ 86

Author(s):

Isidro Peñalver-Martinez ◽

Francisco Garcia-Sanchez ◽

Rafael Valencia-Garcia ◽

Miguel Ángel Rodríguez-García ◽

Valentín Moreno ◽

...

Keyword(s):

Opinion Mining ◽

Feature Based

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Feature-Based Opinion Mining and Managed Machine Learning with Sentiment Classification Models

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4555.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3992-3998

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbors ◽

Data Intensive ◽

Learning Tasks ◽

Feature Based

Sentiment Analysis is individuals' opinions and feedbacks study towards a substance, which can be items, services, movies, people or events. The opinions are mostly expressed as remarks or reviews. With the social network, gatherings and websites, these reviews rose as a significant factor for the client’s decision to buy anything or not. These days, a vast scalable computing environment provides us with very sophisticated way of carrying out various data-intensive natural language processing (NLP) and machine-learning tasks to examine these reviews. One such example is text classification, a compelling method for predicting the clients' sentiment. In this paper, we attempt to center our work of sentiment analysis on movie review database. We look at the sentiment expression to order the extremity of the movie reviews on a size of 0(highly disliked) to 4(highly preferred) and perform feature extraction and ranking and utilize these features to prepare our multilabel classifier to group the movie review into its right rating. This paper incorporates sentiment analysis utilizing feature-based opinion mining and managed machine learning. The principle center is to decide the extremity of reviews utilizing nouns, verbs, and adjectives as opinion words. In addition, a comparative study on different classification approaches has been performed to determine the most appropriate classifier to suit our concern problem space. In our study, we utilized six distinctive machine learning algorithms – Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), RF (Random Forest) KNN (K nearest neighbors) and SoftMax Regression.

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