An Overview of Bag of Words;Importance, Implementation, Applications, and Challenges

Qader, Wisam A.; Ameen, Musa M.; Ahmed, Bilal Ismael

doi:10.1109/iec47844.2019.8950616

Cited by 95 publications

(45 citation statements)

References 15 publications

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“…The paper [14] provides an introduction to BoW, its importance, how it operates, its implementations, and the challenges of utilizing it. This review is helpful in terms of introducing the BoW methodology to new researchers and providing a good context with related work to researchers working on this model.…”

Section: A Baseline Feature Extraction Methodsmentioning

confidence: 99%

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Evaluation of different extractors of features at the level of sentiment analysis

Es-sabery¹,

Es-sabery²,

Garmani³

et al. 2022

Infocommunications journal

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Sentiment analysis is the process of recognizing and categorizing the emotions being expressed in a textual source. Tweets are commonly used to generate a large amount of sentiment data after they are analyzed. These feelings data help to learn about people's thoughts on a various range of topics. People are typically attracted for researching positive and negative reviews, which contain dislikes and likes, shared by the consumers concerning the features of a certain service or product. Therefore, the aspects or features of the product/ service play an important role in opinion mining. Furthermore to enough work being carried out in text mining, feature extraction in opinion mining is presently becoming a hot research field. In this paper, we focus on the study of feature extractors because of their importance in classification performance. The feature extraction is the most critical aspect of opinion classification since classification efficiency can be degraded if features are not properly chosen. A few scientific researchers have addressed the issue of feature extraction. And we found in the literature that almost every article deals with one or two feature extractors. For that, we decided in this paper to cover all the most popular feature extractors which are BOW, N-grams, TF-IDF, Word2vec, GloVe and FastText. In general, this paper will discuss the existing feature extractors in the opinion mining domain. Also, it will present the advantages and the inconveniences of each extractor. Moreover, a comparative study is performed for determining the most efficient combination CNN/extractor in terms of accuracy, precision, recall, and F1 measure.

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Section: A Baseline Feature Extraction Methodsmentioning

confidence: 99%

“…The paper [14] provides an introduction to BoW, its importance, how it operates, its implementations, and the  The discussion of the existing feature extractors in the opinion mining domain.…”

Section: A Baseline Feature Extraction Methodsmentioning

confidence: 99%

Evaluation of different extractors of features at the level of sentiment analysis

Es-sabery¹,

Es-sabery²,

Garmani³

et al. 2022

Infocommunications journal

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show abstract

“…Qader et al [17] proposed a sentiment analyzer for movie reviews to determine what kind of movies users prefer. The Linear Support Vector Machine (SVM) and Naïve Bayes machine learning algorithms were tested on four different movie genre datasets: action, adventure, drama and romance genre.…”

Section: Binary Classificationmentioning

confidence: 99%

“…The algorithm also includes data pre-processing for words reduction. Another sentiment analysis had proposed by [17]. This work compared the performance of Linear SVM and Multinomial Naïve Bayes on the dataset of airline reviews obtained from Twitter.…”

Section: Binary Classificationmentioning

confidence: 99%

Theme Identification using Machine Learning Techniques

Jayady

Antong

2021

JIAE

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With the abundance of online research platforms, much information presented in PDF files, such as articles and journals, can be obtained easily. In this case, students completing research projects would have many downloaded PDF articles on their laptops. However, identifying the target articles manually within the collection can be tiring as most articles consist of several pages that need to be analyzed. Reading each article to determine if the article relates theme and organizing the articles based on themes is time and energy-consuming. Referring to this problem, a PDF files organizer that implemented a theme identifier is necessary. Thus, work will focus on automatic text classification using the machine learning methods to build a theme identifier employed in the PDF files organizer to classify articles into augmented reality and machine learning. A total of 1000 text documents for both themes were used to build the classification model. Moreover, the pre-preprocessing step for data cleaning and TF-IDF feature extraction for text vectorization and to reduce sparse vectors were performed. 80% of the dataset were used for training, and the remaining were used to validate the trained models. The classification models proposed in this work are Linear SVM and Multinomial Naïve Bayes. The accuracy of the models was evaluated using a confusion matrix. For the Linear SVM model, grid-search optimization was performed to determine the optimal value of the Cost parameter.

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“…What made topics models unique among previous models is that they minimize the need for an intelligent agent (like humans) to be involved in the process which caused a revolution in a number of branches of science [35]. Unlike traditional approaches in text processing such as BOW [36], topic models take into account the semantic context of words in their unsupervised training steps [37,38].…”

Section: Topic Modelingmentioning

confidence: 99%

Studying the Evolution of Bitcoin-Related Topics Extracted from an Online Forum

Badlou¹

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Besides all uncommon events of 2020, Bitcoin finally passed 20,000 USD in this year, and grabbed more attention about what is going on cryptocurrency and what will happen next? So far, several researchers used social media data in their works and the role of public opinion especially in the specialized forums on Bitcoin price was proved. To have a better understanding about the discussions in a Bitcoin specialized forums, in this thesis we studied the evolution of discussions in bitcointalk.org, as the main references of discussions related to Bitcoin over the past 10 years. We found 31 different topics in the forum by applying LDA topic modeling and categorized them into 6 categories: Future, General, Mining, Monetary, Regulations, and Technical. Our investigation on this work finds relation between Bitcoin price and the sentiment polarity of selected categories of discussions in the forum.

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An Overview of Bag of Words;Importance, Implementation, Applications, and Challenges

Cited by 95 publications

References 15 publications

Evaluation of different extractors of features at the level of sentiment analysis

Evaluation of different extractors of features at the level of sentiment analysis

Theme Identification using Machine Learning Techniques

Studying the Evolution of Bitcoin-Related Topics Extracted from an Online Forum

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