USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWS

International Journal of Scientific Research in Science, Engineering and Technology

Sentiment is an attitude, thought, or judgment prompted by feeling. Sentiment analysis, which is also known as opinion mining, studies people’s sentiments towards certain entities. Sentiment Analysis isn’t an unfamiliar term anymore. Today, smart phones, high speed Internet and various forums and social networks, have made it very common for people to give voice to their opinions. Therefore, a lot of textual data is available in various forms where people express their opinions. Analysing this data to know the underlying sentiment behind it has also become quite popular these days. Various techniques and applications have been created in the past and even today to perform sentiment analysis. This paper contributes towards understanding some of the modern techniques and in knowing which technique to use under what circumstances. It also studies feature extraction which is an important aspect of sentiment analysis. Feature extraction allows us to identify the features in the given tex...

Social media constitutes a major component of Web 2.0 and includes social networks, blogs, forum discussions, micro-blogs, etc. Users of social media generate a huge volume of reviews and comments on daily basis. These reviews and comments reflect the opinions of users about different issues, such as: products, news, entertainments, or sports. Therefore different establishments may need to analyze these reviews and comments. For examples: It is essential for companies to know the pros and cons of their products or services in the eyes of customers. Governments may want In addition to know the attitude of people towards certain decisions, services, etc. Although the manual analysis of textual reviews and comments can be more accurate than the automatic methods, nonetheless, it is time consuming, expensive, and can be In addition subjective. In addition, the huge amount of data contained in social networks can make it impractical to perform analysis manually. This paper focuses on evaluating social content in Arabic language and contexts. Currently, Middle East is an area rich of major political and social reforms. The social media can be a rich source of information to evaluate such contexts. In this research we developed an opinion mining and analysis tool to collect different forms of Arabic language (i.e. Standard or MSA, and colloquial). The tool accepts comments or opinions as input and generates polarity based outputs related to the comments. For example the output can be whether the comment or review is: (subjective or objective), (positive or negative), and (strong or weak). The evaluation of the performance of the developed tool showed that it yields more accurate results when it is applied on domainbased Arabic reviews relative to general-based Arabic reviews.

This paper presents an innovative approach that explores the role of lexicalization for Arabic sentiment analysis. Sentiment Analysis in Arabic is hindered due to lack of resources, language in use with sentiment lexicons, pre-processing of dataset as a must and major concern is repeatedly following same approaches. One of the key solution found to resolve these problems include applying the extension of lexicon to include more words not restricted to Modern Standard Arabic. Secondly, avoiding pre-processing of dataset. Third, and the most important one, is investigating the development of an Arabic Sentiment Analysis system using a novel rule-based approach. This approach uses heuristics rules in a manner that accurately classifies tweets as positive or negative. The manner in which a series of abstraction occurs resulting in an end-to-end mechanism with rule-based chaining approach. For each lexicon, this chain specifically follows a chaining of rules, with appropriate positioning and prioritization of rules. Expensive rules in terms of time and effort thus resulted in outstanding results. The results with end-to-end rule chaining approach achieved 93.9% accuracy when tested on baseline dataset and 85.6% accuracy on OCA, the second dataset. A further comparison with the baseline showed huge increase in accuracy by 23.85%.

ICONIC RESEARCH AND ENGINEERING JOURNALS, 2025

IRE 1709305 ICONIC RESEARCH AND ENGINEERING JOURNALS 1696 datasets. The findings are not merely aimed at informing the proper selection of effective and applicable NLP techniques in different sentiment analysis tasks, but they are intended to guide innovative frameworks, giving such intelligent and context-aware systems in opinion mining. I.

2011

Sentiment mining and classification plays an important role in predicting what people think about products, places, etc. In this piece of work, using basic NLP Techniques like NGram, POS-Tagged NGram we classify movie and product reviews broadly into two polarities: Positive and Negative. We propose a model to address the problem of determining whether a review is positive or negative, we experiment and use several machine learning algorithms Naive Bayes (NB), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) to have a comparative study of the performance of the method we devised in this work. Along with this we also did negation handling and observed improvements in classification. The algorithm we proposed achieved an average accuracy of 78.32% on movie and 70.06% on multi-category dataset. In this paper we focus on the collective study of Ngram and POS tagged information available in the reviews .

International Journal of Research, 2023

Sentiment analysis, often referred to as opinion mining, is a vital subfield of Natural Language Processing (NLP) that focuses on understanding and classifying sentiments expressed in text data. This paper offers a comprehensive exploration of sentiment analysis, encompassing its methodologies, applications across various domains, and practical implications. We delve into the specifics of data collection, preprocessing, feature extraction, sentiment analysis techniques, and present empirical findings that highlight the effectiveness of our approach.

The tasks that falls under the errands that takes after Natural Language Processing approaches includes Named Entity Recognition, Information Retrieval, Machine Translation, and so on. Wherein Sentiment Analysis utilizes Natural Language Processing as one of the way to locate the subjective content showing negative, positive or impartial (neutral) extremity (polarity). Due to the expanded utilization of online networking sites like Facebook, Instagram, Twitter, Sentiment Analysis has increased colossal statures. Examination of sentiments helps organizations, government and other association to extemporize their items and administration in view of the audits or remarks. This paper introduces an Innovative methodology that investigates the part of lexicalization for Arabic Sentiment examination. The system was put in place with two principles rules– “equivalent to” and “within the text” rules. The outcomes subsequently accomplished with these rules methodology gave 89.6 % accuracy when tried on baseline dataset, and 50.1 % exactness on OCA, the second dataset. A further examination shows 19.5 % in system1 increase in accuracy when compared with baseline dataset.

International Journal of Advanced Computer Science and Applications

In light of the spread of e-commerce and emarketing, and the presence of a huge number of reviews and texts written by people to share views on products, it became necessary to give attention to extracting these opinions automatically and analyzing the feelings of the reviewers. The goal is to obtain reports evaluating products and contribute to improve services at a glance. Sentiment Analysis is a relatively recent study that deals with the processing of natural texts published in web sites and social networks. However, the processing of texts written in the Arabic language is one of the challenges that specialists face because people do not rely on standard Arabic, writing people in spoken/colloquial languages and use various dialects. This paper will present feature-based sentiment analysis for Arabic language which works on text analysis technique that breaks down text into aspects (attributes or components of a product or service), and then allocates each one a sentiment level (positive, negative or neutral).

Feedbacks and Reviews carry a lot of weightage when it comes to E-commerce websites/stores. Reviews have time and again proven to be a key to the decision-making process. They are utterly helpful in improving their product and services. These reviews come from the end-users and the greater the product or system, more the volume of the reviews. Practically it is not possible, or rather time and resource consuming to read each and every review and perform an analysis. For this purpose, it is very helpful to introduce NLP and Machine Learning algorithms to easily analyze the negative reviews from customers and display graphical representations for the users to derive actionable businesscritical insights.

International Journal of Advanced Computer Science and Applications

Customer reviews of products and services play a key role in the customers' decision to buy a product or use a service. Customers' preferences and choices are influenced by the opinions of others online; on blogs or social networks. New customers are faced with many views on the web, but they can't make the right decision. Hence, the need for sentiment analysis is to clarify whether opinions are positive, negative or neutral. This paper suggests using the Aspect-Based Sentiment Analysis approach on reviews extracted from tourism websites such as TripAdvisor and Booking. This approach is based on two main steps namely aspect extraction and sentiment classification related to each aspect. For aspect extraction, an approach based on topic modeling is proposed using the semi-supervised CorEx (Correlation Explanation) method for labeling word sequences into entities. As for sentiment classification, various supervised machine learning techniques are used to associate a sentiment (positive, negative or neutral) to a given aspect expression. Experiments on opinion corpora have shown very encouraging performances.