An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Nafis, Nur Syafiqah Mohd; Awang, Suryanti

doi:10.1109/access.2021.3069001

Cited by 63 publications

(23 citation statements)

References 27 publications

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“…The experimental analysis is performed with different algorithms like PSO [35], GWO [30], WOA [20], and FOA [11]. The performance of the proposed model is also compared over the conventional models such as SVM [21,24], CNN [16], LSTM [22], and DNN [29] in terms of "Type I and Type II measures.…”

Section: Methods Used For Comparisonmentioning

confidence: 99%

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Kayıkçı

2022

Multimed Tools Appl

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Sentiment analysis is one of the efficient models for extracting opinion mining with identification and classification from unstructured text data such as product reviews or microblogs. It is used to gain feedback from political campaigns, brand reviews, marketing analysis, and customers. The sentiment analysis on Twitter data is a recent research field in the natural processing. The dataset is gathered from the "Twitter" package in R along with Twitter API. The main intent of this paper is to understand the public opinion on the recently implemented demonetization policy using the proposed SenDemonNet. Initially, the tweet preprocessing was done, which is intended for cleaning the text data. Then, the feature extraction is performed by Bag of n-grams, TF-IDF, and the word2vec algorithm. The main objective of this work is a weighted feature selection that is developed by the hybrid Forest-Whale Optimization Algorithm (F-WOA) to get the best classification outcome. With these features, the Heuristic Deep Neural Network (HDNN) is adopted for classification, where the proposed FOA and WOA tune the parameter of DNN for reaching the maximum accuracy rate. From the statistical analysis, the performance of the designed F-WOA-DNN is 1.8%, 1.9%, 1.86%, and 2% enhanced than PSO-DNN, GWO-DNN, WOA-DNN, FOA-DNN, SVM, CNN, LSTM, and DNN respectively. Extensive experimental results show that SenDemonNet outperforms its competitors, producing an impressive increase in the classification accuracy on the benchmark dataset.

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Section: Methods Used For Comparisonmentioning

confidence: 99%

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Kayıkçı

2022

Multimed Tools Appl

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“…It's a type of natural language processing where the review comments are categorized as either positive/ or negative. Generally, all observations in the sentimental analysis are labelled only in these two categories whereas the third category is neutral [3,4]. In this work, the third category is highlighted to trigger the irrelevant cases into irrelevant.…”

Section: Sentimental Analysismentioning

confidence: 99%

Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

Selvi¹,

Muneeshwari²,

Selvasheela³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network. The sentiment classification accuracy is evaluated using various selection methods, especially those that deal with algorithm selection. In this work, every sentiment received through user expressions is ranked in order to categorise sentiments as informative and noninformative. In order to do so, the work focus on Query Expansion Ranking (QER) algorithm that takes user text as input and process for sentiment analysis and finally produces the results as informative or non-informative. The challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial, entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine (SVM) and decision trees. The work also addresses simulated annealing along with QER to classify data based on sentiment analysis. As the input volume is very fast, the work also addresses the concept of big data for information retrieval and processing. The result comparison shows that the QER algorithm proved to be versatile when compared with the result of SVM. This work uses Twitter user comments for evaluating sentiment analysis.

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“…Ide dasar TF-IDF adalah memberikan bobot pada setiap kalimat, selanjutnya kalimat tersebut diurutkan berdasarkan bobot teratas dengan bobot paling besar akan dipilih sebagai hasil. Bobot kalimat diperoleh dari penjumlahan bobot term pada sebuah kalimat [15].…”

Section: Pembobotan Kataunclassified

TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets

Priyatno

Ningsih²

2022

SISTEMASI

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Twitter merupakan salah satu layanan media sosial yang sering digunakan (popular) sebagai sarana komunikasi antar pengguna. Kepopuleran twitter tersebut membuat spammer melakukan spam demi tujuan dan keuntungan pribadi. Bot spammer merupakan penyalahgunaan user pada media sosial Twitter. Spammer menyebarkan spam secara bertubi-tubi pada pengguna lain. Spam ini dilakukan bertujuan untuk mencapai trending topik. Aktivitas spam dilakukan dengan meniru pola perilaku pengguna asli agar tidak terdeteksi sebagai tindakan penyalahgunaan Twitter. Penelitian ini mengusulkan pembobotan TF-IDF untuk mendeteksi akun spammer di Twitter berdasarkan tweet dan representasi retweet dari tweet. Tujuan dari penelitian ini adalah untuk mendeteksi Bot Spammer atau Human menggunakan teknik klasifikasi meggunakan algoritma naive bayes. Hasil percobaan terbaik pada pembagian 70% data latih dan 30% data uji mendapatkan akurasi 92% dengan precision dan recall sebesar 100% dan 87.5%. Hal ini menunjukan berhasil mendeteksi akun bot spammer di Twitter.

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An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Cited by 63 publications

References 27 publications

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets

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