Levenstein's Algorithm On English and Arabic: A Survey

Abdulmalek, Sarah; Al-Hagree, Salah; Alsurori, Muneer; Hadwan, Mohammed; Aqlan, Amal; Alqasemi, Fahd

doi:10.1109/ictsa52017.2021.9406547

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…In the Levenshtein distance (LD) algorithm, given two names X and Y represented as strings of n and m characters, respectively, the LD, aka, refers to the minimum cost of editing operations (inserting, deleting, and substituting) to convert X to Y [14], [15], [25]. For example, if X=" Zantac‖" and Y=" Xanax‖", the edit distance is 3, as the minimum transformation implies two substitution operations ("Z" → "X" and "c" → "x") and one deleting operation (letter "t").…”

Section: Levenshtein Distance Algorithmmentioning

confidence: 99%

Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

Al-Shalabi

Al-Hagree

Al-Gaphari

et al. 2023

jast

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The field of sentiment analysis of Arabic scripts remains a major challenge due to the features, characteristics, and complexity of the Arabic language. Few studies on Arabic Sentiment Analysis (ASA) have been conducted to the best of our knowledge when compared to English or other Latin languages. In addition, for ASA, very few studies have been conducted regarding the comments of the users (customers) of the apps available on the Google Play Store within various mobile application reviews. Most of the current studies have been conducted on datasets collected from Twitter user comments. In this paper, we propose a new approach to the analysis of sentiment in Arabic script based on the comments dataset of users of some mobile applications available on the Google Play Store. The proposed approach involves improving algorithms such as the Levenshtein distance (LD) algorithm for data preprocessing and then combining it with the K-Nearest Neighbor (K-NN) algorithm. Through the proposed approach, the results of the experiment were shown, investigating the impact of utilizing the K-NN and LD algorithms for ASA on mobile applications effectively. The experiments were carried out. The K-NN with LD algorithm has gained a better level of evaluation compared to the K-NN algorithm. K-NN with LD algorithm has achieved the highest accuracy, recall, precision, and F-score, which were 83.11% in accuracy, 66.30% in recall, 85.10% in precision, and 74.53% in f-score evaluation measure when =3.

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Section: Levenshtein Distance Algorithmmentioning

confidence: 99%

Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

Al-Shalabi

Al-Hagree

Al-Gaphari

et al. 2023

jast

View full text Add to dashboard Cite

show abstract

“…The distance metric in FM is commonly employed to quantify the dissimilarity between two strings [29], [30]. In this FM, a specific approach that involves calculating the ratio of similarity distance values is used through the Levenshtein distance similarity technique [31]. Word weighting is a technique used to index words by assigning weights to each word, based on its significance in the document.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Nurcahyawati¹,

Mustaffa

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance. Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,

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“…Therefore, it needs spelling correction. Levenshtein Distance, also called edit-distance, is used to find word candidates suggested based on number of minimum characters that need to be substituted, inserted, or deleted to change words from string A to be string B [9]. Levenshtein Distance provides good results in solving problems of matching string data to provide text suggestion, for instance in handwriting recognition, search words and misspelled words, so the input effectiveness increases, misspelling can be avoided and auto-complete accelerates human computer interaction [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spelling Correction and Query Expansion for Relevance Document Searching

Soyusiawaty¹,

Wolley²

2021

IJACSA

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A digital library is a type of information retrieval (IR) system. The existing IR methodologies generally have problems on keyword searching. Some of search engine has not been able to provide search results with partial matching and typographical error. Therefore, it is required to be able to provide search results that are relevant to keywords provided by the user. We proposed a model to solve the problem by combining the spell correction and query expansion. Searching is starting with indexing the title of the document by preprocessing the title of all incoming document data and then weighting the Term Frequency -Inverse Document Frequency (TF-IDF) against all terms of the whole document. Levenshtein Distance algorithm is used in the search process to correct typo-indicated keywords. Before calculating the relevance between the keywords and the documents using Cosine Similarity, the keywords are expanded using Query Expansion to increase number of documents retrieved. Calculation results using Cosine Similarity are then added to Query Expansion weight calculation to get final ranking result. Results show improvements over IR system compared with system without spell check and query expansion. The results of the study in the form of web-based application conducted testing for 50 times with number of data of 2,045. The system was able to correct typo-indicated keywords and search documents with average recall value of 95.91%, average precision value of 63.82% and average Non Interpolated Average Precision (NIAP) value of 86.29%.

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Levenstein's Algorithm On English and Arabic: A Survey

Cited by 5 publications

References 30 publications

Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Hybrid Spelling Correction and Query Expansion for Relevance Document Searching

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