The Impact of Features Extraction on the Sentiment Analysis

Ahuja, Ravinder; Chug, Aakarsha; Kohli, Shruti; Gupta, Shaurya; Ahuja, Pratyush

doi:10.1016/j.procs.2019.05.008

Cited by 221 publications

(120 citation statements)

References 14 publications

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“…TF-IDF is a well-known method to evaluate a word's importance in a text and multiplication of term frequency (TF) and the inverse document frequency (IDF). (TF) of a particular term (t) is calculated as the number of times a term occurs in a document to the total number of words in the text (Ahuja et al, 2019). IDF is the log of the inverse probability of a term being in the text.…”

Section: Figure 3 An Example Of Bag-of-words Representationmentioning

confidence: 99%

Turkish Sentiment Analysis for Open and Distance Education Systems

Aydin¹,

Öztürk²,

Cicek³

2021

Turkish Online Journal of Distance Education

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Students' opinions are the most essential source to enhance the quality of education and educational services in Open and Distance education (ODE) Systems. How to access and analyze students' real opinions is a problem for ODE institutions. The purpose of the present study is to conduct a sentiment analysis (SA) on the collected Turkish tweets about an ODE system to monitor students' opinions and sentiments about the system. Firstly, the related 63699 tweets about the ODE system are gathered and analyzed. Later, preprocessing is applied to the dataset. Sentence-based SA is performed with the data provided. The dataset is vectorized using two vector space models to test four classifiers which are Support Vector Machines, K-Nearest Neighbor, Logistic Regression (LR), and Artificial Neural Networks. F-score values obtained with these classifiers are evaluated, and the results are discussed. LR classifier gives the best F-score values with %75 for each vector space model. Through the SA results, students' dissatisfaction, appreciation, and concerns will be learned quickly by the university administration to develop strategies that will increase the quality of education and educational services.

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Section: Figure 3 An Example Of Bag-of-words Representationmentioning

confidence: 99%

Turkish Sentiment Analysis for Open and Distance Education Systems

Aydin¹,

Öztürk²,

Cicek³

2021

Turkish Online Journal of Distance Education

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“…erefore, feature extraction techniques are highly required to furtherly refine the feature subset [20]. e authors in [21] propose a feature selection method for Arabic text classification using an improved chi-square to enhance the performance of classification.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Selection for Amharic News Document Classification

Endalie

Haile

2021

Mathematical Problems in Engineering

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Today, the amount of Amharic digital documents has grown rapidly. Because of this, automatic text classification is extremely important. Proper selection of features has a crucial role in the accuracy of classification and computational time. When the initial feature set is considerably larger, it is important to pick the right features. In this paper, we present a hybrid feature selection method, called IGCHIDF, which consists of information gain (IG), chi-square (CHI), and document frequency (DF) features’ selection methods. We evaluate the proposed feature selection method on two datasets: dataset 1 containing 9 news categories and dataset 2 containing 13 news categories. Our experimental results showed that the proposed method performs better than other methods on both datasets 1and 2. The IGCHIDF method’s classification accuracy is up to 3.96% higher than the IG method, up to 11.16% higher than CHI, and 7.3% higher than DF on dataset 2, respectively.

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“…The proposed technique reflects an increase of 5% accuracy yielding to 73% for NB classifier. R. Ahujaa et al [50] implemented six classifiers viz. Decision Tree, SVM, KNN RF, LR TF-IDF, NB by using two feature selection techniques N-gram and TF-IDF on 'SS-Tweets' data set.…”

Section: Research Strategy Designmentioning

confidence: 99%

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Rani¹,

Singh²,

Gulia³

2021

IJACSA

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Social media has rapidly expanded over a period of time and generated a huge repository of content. Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement. Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis. This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis. The paper explores and identifies the techniques and tools used in a well-structured approach to find out the research gaps and identify future scope in this area of research. The techniques evolved over time to improve the efficiency of classification. Total 55 research papers are included in this survey. The result reflects that Twitter is the most explored social networking site for opinion mining. Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches. As the latest advancements, Stack based ensemble, fuzzy based and neural network based classifiers are also implemented to enhance the efficiency of classification. WEKA, R Studio, Python are mostly used tools by research scholars for implementation. The overall evolution of the research goes through various changes in terms of technologies, tools, social media platforms and data corpus targeted.

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The Impact of Features Extraction on the Sentiment Analysis

Cited by 221 publications

References 14 publications

Turkish Sentiment Analysis for Open and Distance Education Systems

Turkish Sentiment Analysis for Open and Distance Education Systems

Hybrid Feature Selection for Amharic News Document Classification

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

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