Name
Title | ||
|---|---|---|
Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis. |
Abstract | ||
|---|---|---|
Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine SVM, Maximum Entropy ME and Naive Bayes NB, have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJKDB.2017010103 | IJKDB |
Field | DocType | Volume |
Audit,Naive Bayes classifier,Sentiment analysis,Computer science,Support vector machine,Premise,Artificial intelligence,n-gram,Principle of maximum entropy,Machine learning | Journal | 7 |
Issue | Citations | PageRank |
1 | 4 | 0.47 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prayag Tiwari | 1 | 4 | 0.47 |
| Brojo Kishore Mishra | 2 | 6 | 3.55 |
| Sachin Kumar | 3 | 4 | 1.15 |
| Vivek Kumar | 4 | 4 | 0.47 |