A survey of sentiment analysis techniques

Kaur, Harpreet; Mangat, Veenu; Nidhi, -

doi:10.1109/i-smac.2017.8058315

Cited by 85 publications

(47 citation statements)

References 11 publications

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“…As it has been well established that customer reviews play a crucial role in swaying the mood of public be it in the matter of choosing a product, opting to watch a movie and similar matters it is important to use the resources at hand to analyze and summarize these reviews to provide a concise output and recommendation to the users so as to help them make better decision. After surveying for the various methodologies for performing Sentiment Analysis and Opinion Summarization of a customer review, it can be concluded that so far Support Vector Machine would be the appropriate choice for the categorization [3], while any of the approaches mentioned above for Lexicon classification are equally viable [2]. Also, Latent Semantic Approach [4] seems most reasonable for feature identification.…”

Section: Discussionmentioning

confidence: 99%

“…[2], is basically a survey paper for [1] and is full of details about sentiment classification and polarity check and on the use of SVM, Naive Bayes and Maximum Entropy and throws light on machine learning methods to accomplish Sentiment classification. In [3], points at the various approaches and methodologies for Sentiment Classification. These methods include 1.…”

Section: Related Workmentioning

confidence: 99%

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Sentiment Analysis and Opinion Summarization of Product Feedback

2019

ijrte

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With the exponential growth of online shopping platforms, user interaction is made direct through their reviews and ratings. User’s opinions and experiences are a significant source of valuable information in decision making process. In recent days, almost every website encourages users to express and exchange their views, suggestions and opinions related to product, services, policies, etc. publicly. Opinion mining is an extensive branch of Artificial Intelligence and a form of Natural Language Processing which illustrates the attitude of the customers, in specific services or products. Also known as Sentiment Analysis, it aims at determining the response and mood or attitude of the speaker or the overall contextual and emotional polarity or reaction. Existing algorithms determine sentiment by training on datasets, lexicon-based approach by calculating polarity and rule-based approach for classification. Opinion Summarization is the process of consolidating a large amount of sentiments and opinions into a clear and brief statement for an easier grasp on the underlying context. Major summarization methods include, Extractive method, Sentence Ranking, Abstractive method and Clustering of Textual Segments. Hence it is important to judge and classify these reviews and present a laconic opinion so it would be easier for users to obtain a gist and overall polarity on the various reviews instead of going through all of them

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis and Opinion Summarization of Product Feedback

2019

ijrte

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“…The process of sentiment analysis in this study refers to [5]. Figure 1 as a research methodology framework, where this study begins with data collecting through the tweet data crawling process to obtain document data.…”

Section: Methodsmentioning

confidence: 99%

“…The activity of opinion mining is computation study to detect and express opinion, sentiment, evaluation, behaviour, emotion, subjectivity, assessment, or view in a current text [3]. Sentiment analysis is the process of exploring the opinions of a text on a particular object [4] [5]. This exploration is carried out to see a person's opinion tendency will have a positive or negative opinion tendency.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Towards Courier Service: Case Study on JNE Semarang

Udayanti¹,

Rahman²,

Kartikadharma³

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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Society as customers often gives an opinion about the company's product and service. Opinion delivered by customers can be both in positive and negative judgments. It is in the form of opinions that describe a customer's emotional expression. Therefore, it is important for companies to be able to understand customers' emotional tendencies from the opinions of texts submitted online. In this study, data text is used in the form of opinions of users of JNE Semarang courier services to conduct sentiment analysis by mining opinions from customer reviews. This research generally proposes the implementation of sentiment analysis of JNE customer's opinion by using K-nearest Neighbor algorithm to classify customer's opinion regarding JNE services. The Confusion Matrix model is adapted to measure the accuracy of the classification results. And the results show that majority opinions classify into negative sentiment with the highest accuracy on value k=7.

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“…The concept is used to extract pros and cons regarding hotels, movies, products, student performance and so on. Sentiment Analysis procedure is continuously finding out the sentiment, if our text is having Subjectivity [1]. Hence sentiment analysis is the suitable method to analyze the astrological belief opinions.…”

Section: B Sentiment Analysismentioning

confidence: 99%

Identification of Astrological belief using Sentimental Analysis by Capturing Opinions from Cross-Domain Individuals

Gowrinath*,

Kumar,

Dutt

2020

IJRTE

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Astrology usage is substantially composed of the belief and opinions by individuals in recent times. Astrology describes a huge set of predictions by using a wide range of horoscope charts along with scientific/mathematical computations. Planets movement around twelve houses is the vital thing for Astrological specifications. Astrology is having mutual sides of belief in proportionate and the belief is identified by using concepts like Sentiment analysis, Naïve Bayes classifiers and others. Sentiment Analysis specifies the odds in-favor and against the concept. The current study describes a model to identify the belief in and against cases on Astrological belief; also identifies the deviated opinions with the help of various features using confusion matrix. Training, Tuning and other analytical activities are used to build and verify a model for accuracy. The present study is mainly emphasized on identifying the belief, which is very useful to derive an unambiguous thesis on Astrology in nearby future.

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A survey of sentiment analysis techniques

Cited by 85 publications

References 11 publications

Sentiment Analysis and Opinion Summarization of Product Feedback

Sentiment Analysis and Opinion Summarization of Product Feedback

Sentiment Analysis Towards Courier Service: Case Study on JNE Semarang

Identification of Astrological belief using Sentimental Analysis by Capturing Opinions from Cross-Domain Individuals

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