Comparative Study on Keyword Extraction Algorithms for Single Extractive Document

Ganiger, Shweta; Rajashekharaiah, K.M.M.

doi:10.1109/iccons.2018.8663040

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The dataset was classified using an algorithm tested using the NB classifier, which was used to evaluate the model [19]. The model uses the pre-labeled data found in the training set to the dataset before deciding its categorization [20]. The NB theorem is a method for computing an event's probability using the probabilistic joint distribution of previous events.…”

Section: Naïve Bayes Classification Modelmentioning

confidence: 99%

“…When the first and second phases of BOW are combined, a statistical metric known as the Term Frequency -Inverse Document Frequency (TF-IDF) is used to evaluate how important a given word is to the content of a given document [20]. The TF-IDF weight is utilized in both the process of information retrieval and text mining.…”

Section: Naïve Bayes Classification Modelmentioning

confidence: 99%

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Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia

Khyrina Airin Fariza Abu Samah,

Nur Shahirah Jailani,

Raseeda Hamzah

et al. 2024

ARASET

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Twitter has become a popular platform for the citizens of Malaysia. Twitter’s ease of expressing opinions could be used to evaluate and review Malaysian Online Food Delivery (OFD) providers. Due to competition from other OFDs in Malaysia, companies need to know customer feedback. OFD reviews are unstructured and massive, making comparisons difficult. Next, some websites evaluate OFD yet only consider pricing, delivery time, and customer experience. Customers cannot visualize the comparison based on users’ preferences, bilingual reviews, and less time-consuming to visualize OFD using the website. Thus, this study aims to design a web application system that uses Naïve Bayes to categorize Twitter sentiment analysis (SA) on Malaysia’s best OFD. It is based on customer satisfaction, visualizing the results, developing the system, and evaluating its accuracy, functioning and usability. Users can read about specific OFD by viewing Twitter SA visualization or comparing them directly. Five aspect-based SA types were presented: affordable price, promotion and discount, review rating, delivery time and condition of food delivered. Functionality testing demonstrated the accomplishment of all objectives. The training and testing data could predict OFD’s Twitter sentiment with 71.67% and 76.29% accuracy for English and Bahasa Melayu, respectively. The system’s usability produced a 94.64% average score using System Usability Scale and was considered “excellent”. Thus, it can be concluded that this study can solve the mentioned issues of OFD and ease the aspect-based comparison.

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Section: Naïve Bayes Classification Modelmentioning

confidence: 99%

Section: Naïve Bayes Classification Modelmentioning

confidence: 99%

Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia

Khyrina Airin Fariza Abu Samah,

Nur Shahirah Jailani,

Raseeda Hamzah

et al. 2024

ARASET

View full text Add to dashboard Cite

show abstract

“…Assessing multiple text documents is a time-consuming and challenging task. Therefore, metadata extraction techniques have a vital role in Text Mining [3]. The extractive summarization gives actual lines but does not contain the whole theme of the written text.…”

Section: Introductionmentioning

confidence: 99%

“…Natural Language Processing (NLP) has numerous real-life applications that require text mining in identifying the text necessary [3,7]. In these applications, some issues regarding metadata processing exist like length, type, diversity [9,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Saeed¹,

Awais²,

Younas³

et al. 2021

Computers, Materials &Amp; Continua

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Text Summarization is an essential area in text mining, which has procedures for text extraction. In natural language processing, text summarization maps the documents to a representative set of descriptive words. Therefore, the objective of text extraction is to attain reduced expressive contents from the text documents. Text summarization has two main areas such as abstractive, and extractive summarization. Extractive text summarization has further two approaches, in which the first approach applies the sentence score algorithm, and the second approach follows the word embedding principles. All such text extractions have limitations in providing the basic theme of the underlying documents. In this paper, we have employed text summarization by TF-IDF with PageRank keywords, sentence score algorithm, and Word2Vec word embedding. The study compared these forms of the text summarizations with the actual text, by calculating cosine similarities. Furthermore, TF-IDF based PageRank keywords are extracted from the other two extractive summarizations. An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed. This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document. This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text. It also solves the issue of deciding the number of representative keywords for a specific text document. To evaluate the technique, the study used a sample of more than eighteen hundred text documents. The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization. The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

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Extractive Summarization Using Frequency Driven Approach

Kalyan

Santhaiah

Nikhil

et al. 2021

Machine Learning Technologies and Applications

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Comparative Study on Keyword Extraction Algorithms for Single Extractive Document

Cited by 6 publications

References 12 publications

Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia

Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Extractive Summarization Using Frequency Driven Approach

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