stdClass Object
(
    [id] => 15542
    [paper_index] => 202503-02-020707
    [title] => SENTIMENT ANALYSIS OF PRODUCT REVIEWS USING NATURAL LANGUAGE PROCESSING
    [description] => 
    [author] => Ms. Sathyapriya V P, Dr.P.Deepika
    [googlescholar] => 
    [doi] => 
    [year] => 2025
    [month] => April
    [volume] => 10
    [issue] => 4
    [file] => fm/jpanel/upload/2025/April/202503-02-020707.pdf
    [abstract] => The project "Sentiment Analysis of Product Reviews" employs the natural language processing (NLP) process and machine learning algorithm to scan and classify customer reviews based on their sentiment as positive, negative, or neutral and how AI can trace consumer behaviour and contribute significantly to customer happiness and decisions. It begins with data collection and preprocessing to prepare textual data for analysis, then followed by feature extraction to identify sentiment patterns precisely. Sentiment classification is accomplished using highly robust machine learning models trained on carefully labelled datasets to ensure reliability and accuracy of classification. The project's performance is thoroughly tested using corresponding metrics to ensure its utility and accuracy. By employing advanced data analysis techniques, this project empowers businesses to improve their goods, services find the next big trends and meet customer’s needs more effectively. In addition, it is a scalable and cost-effective means of automating the process of analysing large amounts of customer reviews, thus being a must-have application for e-commerce sites as well as other consumer-driven industries. 
    [keywords] => Sentiment Analysis, Product Reviews, Natural Language Processing, Machine Learning, Consumer Behaviour, Customer Satisfaction, Data Analysis, Feature Extraction, Sentiment Classification, Labelled Datasets, Performance Evaluation, E-commerce, Data-Driven Decision Making, AI in Analytics, Automated Review Analysis.
    [doj] => 2025-04-09
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 9
    [journal_id] => 2
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Research & Development (IJRD)
    [short_code] => IJSR
    [eissn] =>  2455-7838 (Online)
    [pissn] => - -
    [home_page_wrapper] => images/products_image/2-n.png
)
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