Summarization of customer reviews for a product on a website using natural language processing

Hanni, Akkamahadevi; Patil, Pradeep M.

doi:10.1109/icacci.2016.7732392

Cited by 17 publications

(4 citation statements)

References 2 publications

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“…The hybrid feature vector is then constructed using the RRF and ARF vectors without losing generality. For each input review, the hybrid feature (HF) vector is formed by the concatenation function as (4).…”

Section: End 31 Endmentioning

confidence: 99%

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Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Kaur,

Sharma

2024

IJECE

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Customer review summarization (CRS) offers business owners summarized customer feedback. The functionality of CRS mainly depends on the sentiment analysis (SA) model; hence it needs an efficient SA technique. The aim of this study is to construct an SA model employing deep learning for CRS (SADL-CRS) to present summarized data and assist businesses in understanding the behavior of their customers. The SA model employing deep learning (SADL) and CRS phases make up the proposed automatic SADL-CRS model. The SADL consists of review preprocessing, feature extraction, and sentiment classification. The preprocessing stage removes irrelevant text from the reviews using natural language processing (NLP) methods. The proposed hybrid approach combines review-related features and aspect-related features to efficiently extract the features and create a unique hybrid feature vector (HF) for each review. The classification of input reviews is performed using a deep learning (DL) classifier long short-term memory (LSTM). The CRS phase performs the automatic summarization employing the outcome of SADL. The experimental evaluation of the proposed model is done using diverse research data sets. The SADL-CRS model attains the average recall, precision, and F1-score of 95.53%, 95.76%, and 95.06%, respectively. The review summarization efficiency of the suggested model is improved by 6.12% compared to underlying CRS methods.

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Section: End 31 Endmentioning

confidence: 99%

“…The analysis of the emotions expressed in text for a specific entity or subject is called sentiment analysis (SA) [4]. SA can be categorized as: word level, phrase level, sentence, and document level.…”

Section: Introductionmentioning

confidence: 99%

Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Kaur,

Sharma

2024

IJECE

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“…Text is the standard method for the exchange of information. • Natural language processing: natural language processing (NLP) [4][5][6] deals with how computers understand human languages. It is a field that is studied in computer science, artificial intelligence, and computational linguistics.…”

Section: Terminologymentioning

confidence: 99%

Social Media Analytics: Techniques, Tools, Platforms a Comprehensive Review

Ahuja

Lakhanpal

Kumar

2020

Lecture Notes in Electrical Engineering

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To determine which social media analytics tools, techniques, and platforms were developed in recent times, this paper reviews tools, techniques, and platforms related to social media analytics. In this paper, we talk about the tools used to deal with various social media data (social networking, media, etc.). In the past decade, there has been advancement in the technologies used to deal with social media as there has been an increase in the number of people using social media to share information and also the development of the new social media platforms that have let to increase in the amount of data that we have to deal with. Social media platforms have a considerable number of users across the world, which is overgrowing. These people are sharing information through these sources.There is a large quantity of social data comprising of data related to users, videos, web-based relations, and interactions, etc. which needs to be analyzed. Therefore analyzing social media data has become a significant activity for researchers, mainly due to the availability of the web-based API from social media platforms like twitter, facebook [1], Gmail, etc. This has also led to the development of data services, software tools for analyzing social media data. In this paper, there is a detailed review of the leading software tools and techniques that are used for scraping, cleaning, and analyzing social media data.

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“…Researchers have been working intensively on developing modeling frameworks that can help mine these corpora of information using Artificial Intelligence (AI), Natural Language Processing (NLP), Sentiment Analysis, Information Extraction, and Information Retrieval (Hanni et al 2016). At present various methods have been introduced since the last decade, however, large volume, accuracy, and scalability is a major challenge to performing the CRS.…”

Section: Introductionmentioning

confidence: 99%

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Kaur

Sharma

2022

J Ambient Intell Human Comput

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The reviews posted online by the end-users can help the business owners obtain a fair evaluation of their products/services and take the necessary steps. However, due to the large volume of online reviews being generated from time to time, it becomes challenging for business owners to track each review. The Customer Review Summarization (CRS) model that can present the summarized information and offer businesses with significant acumens to understand the reason behind customers' choices and behavior, would therefore be desirable. We propose the Hybrid Analysis of Sentiments (HAS) for the perspective of effective CRS in this paper. The HAS consists of steps like pre-processing, feature extraction, and review classification. The pre-processing phase removes the unwanted data from the text reviews using Natural Language Processing (NLP) based on different pre-processing functions. For efficient feature extraction, the hybrid mechanism consisting of aspect-related features and review-related features is proposed to build the unique feature vector for each customer review. Review classification is performed using different supervised classifiers like Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The experimental results show that HAS efficiently performed the sentiment analysis and outperformed the existing state-of-the-art techniques with an F1 score of 92.2%.

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Summarization of customer reviews for a product on a website using natural language processing

Cited by 17 publications

References 2 publications

Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Social Media Analytics: Techniques, Tools, Platforms a Comprehensive Review

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

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