Classification of Customer Reviews based on Sentiment Analysis

Gräbner, Dietmar; Zanker, Markus; Fliedl, Günther; Fuchs, Matthias

doi:10.1007/978-3-7091-1142-0_40

Cited by 93 publications

(56 citation statements)

References 17 publications

(27 reference statements)

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“…However, the work of Graebner et al [14] is also focusing on predicting users' rating values. In contrast to our work, Graebner et al exploit the users' textual reviews in order to predict the overall rating value.…”

Section: Related Workmentioning

confidence: 99%

Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Fuchs

Zanker

2012

Lecture Notes in Business Information Processing

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Abstract. Most recommendation systems require some form of user feedback such as ratings in order to make personalized propositions of items. Typically ratings are unidimensional in the sense of consisting of a scalar value that represents the user's appreciation for the rated item. Multi-criteria ratings allow users to express more differentiated opinions by allowing separate ratings for different aspects or dimensions of an item. Recent approaches of multi-criteria recommender systems are able to exploit this multifaceted user feedback and make personalized propositions that are more accurate than recommendations based on unidimensional rating data. However, most proposed multi-criteria recommendation algorithms simply exploit the fact that a richer feature space allows building more accurate predictive models without considering the semantics and available domain expertise. This paper contributes on the latter aspects by analyzing multi-criteria ratings from the major etourism platform, TripAdvisor, and structuring raters' overall satisfaction with the help of a Penalty-Reward Contrast analysis. We identify that several a-priori user segments significantly differ in the way overall satisfaction can be explained by multi-criteria rating dimensions. This finding has implications for practical algorithm development that needs to consider different user segments.

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Section: Related Workmentioning

confidence: 99%

Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Fuchs

Zanker

2012

Lecture Notes in Business Information Processing

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“…Graebner et.al. [6] proposed a system that performs the classification of customer reviews of hotels by means of a sentiment analysis. They used a corpus to extract the domain specific lexicon to be used in classification and classified reviews as positive or negative.…”

Section: Related Workmentioning

confidence: 99%

Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification

Dehkharghani

Yanıkoğlu

Tapucu

et al. 2012

2012 IEEE 12th International Conference on Data Mining Workshops

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Abstract-Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively; improving the baseline using just the average word polarities by about 2% points.

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“…Sentiment analysis involves in mining the naturally expressed text to understand the feeling of people towards the entity of interest. Sentiment mining and analysis has found many application in areas of healthcare [3], [4], tourism [5], fraud detection [6], finance [7], politics [8], business [9], few more applications are listed in [10]. In [11] informatics, theoretic approach is used for classification of sentiments.…”

Section: Introductionmentioning

confidence: 99%

3D Visualization of Sentiment Measures and Sentiment Classification using Combined Classifier for Customer Product Reviews

Urologin¹,

Thomas²

2018

ijacsa

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Abstract-TheInternet has wide reachability making many users to buy the products online using e-commerce websites. Usually, users provide their opinions, comments, and reviews about the products in social media, e-commerce websites, blogs, etc. The product review comments provided by the customers have rich information about the usage of the products they bought and their sentiments towards those products. In this research, we have collected reviews from Amazon.com and performed sentiment analysis to collect sentiment information. We have proposed 3D visualizations to represent sentiment information, such as sentiment scores and statistics about words used in the reviews. The 3D visualizations are useful to represent large sentiment related information and to have an in-depth understanding of sentiments of users. We have developed a combined classifier using Logistic Regression, Decision Tree and Support Vector Machine. From the reviews, we formed N-gram features using a bag of words and performed sentiment classification using combined classifier. On 10 fold crossvalidation, a maximum classification rate for combined classifier of 90.22% is obtained for sentiment classification.

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Classification of Customer Reviews based on Sentiment Analysis

Cited by 93 publications

References 17 publications

Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification

3D Visualization of Sentiment Measures and Sentiment Classification using Combined Classifier for Customer Product Reviews

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