Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Customer Review Summarization using Hybrid Analysis of Sentiments

Customer Review Summarization using Hybrid Analysis of Sentiments

Kaur, Gagandeep
Pinjarkar, Latika
Agrawal, Poorva

Paperback

Technology & Engineering

ISBN10: 6207805356
ISBN13: 9786207805358
Publisher: LAP Lambert Academic Publishing
Published: Jun 14 2024
Pages: 132
Weight: 0.45
Height: 0.31 Width: 6.00 Depth: 9.00
Language: English
The rapid increase in textual content generation from sources like WhatsApp, Instagram, and Amazon produces massive amounts of data daily. Interpreting this data can help business owners understand the public perception of their products or services and make informed decisions. Due to the large volume of text, Natural Language Processing (NLP), a crucial aspect of Sentiment Analysis (SA), is essential for content interpretation. This research focuses on developing a Consumer Review Summarization (CRS) model using NLP techniques and Long Short-Term Memory (LSTM) to summarize data and provide businesses with significant insights into consumer behavior and preferences. The CRS model's effectiveness relies on the SA model and consists of two phases: SADL and CRS. The SADL phase includes review preprocessing, feature extraction, and sentiment classification, while the CRS phase performs automatic summarization based on the SADL outcomes.

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