Sentiment Analysis of Amazon Food Review Data
| Autor: | Sneha Choudhary, Charu Chhabra |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
Computer science business.industry Bigram Sentiment analysis Decision tree Confusion matrix Perceptron computer.software_genre Statistical classification ComputingMethodologies_PATTERNRECOGNITION Categorization Artificial intelligence business computer Natural language processing |
| Zdroj: | 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT). |
| DOI: | 10.1109/ccict53244.2021.00033 |
| Popis: | Sentiment classification is a functioning examination area in information mining and information disclosure with different application spaces. Accomplishment of item advancement sites, for example, Amazon, eBay and so forth gets influenced by the nature of the surveys they have for their yields. Every one of these destinations gives a path to the analyst to form the remarks on the basis of the items and assign a remark to it. Considering these remarks, the analysis will be classified as best or worst. By this, a structure can be edified that can identify the sentiment masked in a review, performing sentiment categorization on a gigantic dataset. All particulars can be grouped into primarily two classes, facts and opinions. Facts are assertions about matter and worldly occurrences. And opinions are individual statements that mirror individuals’ assumptions or bits of knowledge about the entities and events. This paper shows the performance of classification algorithms such as Decision Tree, Bernoulli NB, Logistic, and Perceptron using Principal Component Analysis (PCA), applying n-gram (unigram, bigram) on the entire feature set and computing confusion matrix for the dataset. |
| Databáze: | OpenAIRE |
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