Sentiment Analysis Approach Based on N-gram and KNN Classifier

Kaur, Sumandeep; Sikka, Geeta; Awasthi, Lalit Kumar

doi:10.1109/icsccc.2018.8703350

Cited by 29 publications

(14 citation statements)

References 7 publications

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“…In the research done by Kaur et al [9], the proposed system uses k-nearest neighbors (KNN) as a classifier for classifying sentiments of text on e-commerce sites into positive, negative, and neutral sentiments on tweeter dataset. Features generated using n-gram before the KNN classifier took place.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Hybrid Feature Selection Techniques

Abdulkhaliq

Darwesh

2020

UHD J SCI TECH

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Nowadays, people from every part of the world use social media and social networks to express their feelings toward different topics and aspects. One of the trendiest social media is Twitter, which is a microblogging website that provides a platform for its users to share their views and feelings about products, services, events, etc., in public. Which makes Twitter one of the most valuable sources for collecting and analyzing data by researchers and developers to reveal people sentiment about different topics and services, such as products of commercial companies, services, well-known people such as politicians and athletes, through classifying those sentiments into positive and negative. Classification of people sentiment could be automated through using machine learning algorithms and could be enhanced through using appropriate feature selection methods. We collected most recent tweets about (Amazon, Trump, Chelsea FC, CR7) using Twitter-Application Programming Interface and assigned sentiment score using lexicon rule-based approach, then proposed a machine learning model to improve classification accuracy through using hybrid feature selection method, namely, filter-based feature selection method Chi-square (Chi-2) plus wrapper-based binary coordinate ascent (Chi-2 + BCA) to select optimal subset of features from term frequency-inverse document frequency (TF-IDF) generated features for classification through support vector machine (SVM), and Bag of words generated features for logistic regression (LR) classifiers using different n-gram ranges. After comparing the hybrid (Chi-2+BCA) method with (Chi-2) selected features, and also with the classifiers without feature subset selection, results show that the hybrid feature selection method increases classification accuracy in all cases. The maximum attained accuracy with LR is 86.55% using (1 + 2 + 3-g) range, with SVM is 85.575% using the unigram range, both in the CR7 dataset.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Hybrid Feature Selection Techniques

Abdulkhaliq

Darwesh

2020

UHD J SCI TECH

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“…The sentiment analysis method based on traditional machine learning often trains a sentiment classifier through a given dataset, and then uses the sentiment classifier to predict sentiment polarity. Specifically, sentiment features are often first represented by statistical algorithms such as bag of words (BOW) [ 14 ], TF-IDF [ 15 ], N-grams [ 16 ], etc., and then the classifier is trained, and finally the classifier is used to predict sentiment polarity. In machine learning, common sentiment classifiers include support vector machine (SVM) [ 17 ], naive Bayes (NB) [ 18 ], and maximum entropy.…”

Section: Introductionmentioning

confidence: 99%

“…Huq et al [ 19 ] used KNN and SVM to classify the sentiment polarity of Twitter text. Dey et al [ 16 ] used n-gram for feature extraction and added tags after the sentence, and then used the SVM classification algorithm for sentiment classification. Although some progress has been made in sentiment analysis based on traditional machine learning, due to the limitations of this method itself, it cannot represent sentiment features well, and the use of emotional information in the training process is limited.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Wang¹,

Meng²

2022

Sensors

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In Chinese sentiment analysis tasks, many existing methods tend to use recurrent neural networks (e.g., long short-term memory networks and gated recurrent units) and standard one-dimensional convolutional neural networks (1D-CNN) to extract features. This is because a recurrent neural network can deal with the order dependence of the data to a certain extent and the one-dimensional convolution can extract local features. Although these methods have good performance in sentiment analysis tasks, recurrent neural networks (RNNs) cannot be parallelized, resulting in time-inefficiency, and the standard 1D-CNN can only extract a single sample feature, with the result that the feature information cannot be fully utilized. To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis. Firstly, we no longer use word encoding technology but use character-based integer encoding to retain more fine-grained information. Besides, in character-level vectors, the interactive features of different elements are introduced to improve the dimensionality of feature vectors and supplement semantic information so that the input matches the model network. In order to ensure that more sentiment features are learned, group strategies are used to form several feature mapping groups, so the learning object is converted from the traditional single sample to the learning of the feature mapping group, so as to achieve the purpose of learning more features. Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales. The experimental results on the Chinese dataset show that our proposed method outperforms other baseline and state-of-the-art methods.

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“…Among different machine classifiers, mostly SVM classifiers are found to be effective for such tasks [13]. Few other models used for sentiment analysis tasks are maximum entropy classifiers [14], KNN classifiers [15], [16], Naïve bayes [7]. The ensemble of different classifiers and different feature sets was also proposed [2] for the sentiment analysis task.…”

Section: Introductionmentioning

confidence: 99%

Polarity Detection and Feature-Based Summarization for Aspect Based Sentiment Analysis Dataset

2022

IJIES

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Aspect Based Sentiment Analysis (ABSA) aims to perceive the opinion of the majority of reviewers for each feature (aspect category) of the product. We target both Aspect Term Polarity (ATP) and Aspect Category Polarity (ACP) subtasks of ABSA. We retrieve different word vector-based representations of sentence and augment them with n-gram and sentiment features using novel algorithm. We employ supervised machine learning approaches and propose the ensemble of Logistic Regression, k-Nearest Neighbor, and Support Vector Machine models for these subtasks. We experiment with four major domains from Hindi ABSA Dataset and report increase in performance accuracy by 16% for ATP subtask and 2% to 13% across domains for ACP subtask over results from researchers who released this dataset. Secondly, we also propose a novel WFBOS method to compute the Summary Score [range: -100 to 100] of each category from a particular domain. We make use of frequency of positive, neutral, negative, and conflict sentiment, of each aspect category from among the reviews of a particular domain in Hindi ABSA Dataset to do this computation.

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Sentiment Analysis Approach Based on N-gram and KNN Classifier

Cited by 29 publications

References 7 publications

Sentiment Analysis Using Hybrid Feature Selection Techniques

Sentiment Analysis Using Hybrid Feature Selection Techniques

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Polarity Detection and Feature-Based Summarization for Aspect Based Sentiment Analysis Dataset

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