Summarizing Customer Reviews Based on Feature Extraction and Opinion Mining for Online Products
S. Prem Kumar, M. Janardhan, V. Sidda Reddy, S. A. Suma · 2024
In the past few years, online shopping has rapidly increased due to flexible shopping and purchases from home, as well as web shopping portals rapidly growing. Customer reviews on various products and their features in the form of opinion mining are an important research area and present challenges to mining unstructured text data. Potential customers can benefit from customer feedback, but product manufacturers can also enhance their products due to reviews. The various product characteristics may be discussed in each of these reviews. This analysis discusses Summarizing Customer Reviews for Online Products Using Feature Extraction and Opinion Mining. The positive and negative polarities of each product feature are summarized using this method. They use association rule mining to determine a product’s most distinctive characteristics. Feature extraction and polarity classification are the two processes that make up the proposed algorithm’s performance. The popularity of the opinion words connected to the feature has been assessed using a final summary generated by the sentiment lexicon. The experiment result shows that the proposed method outperforms previous works for the extraction of opinion phrases and product attributes in terms of performance precision, recall, accuracy, and F1-Score.