Enhancing Lexicon Based Sentiment Analysis Using n-gram Approach

Farah, Hassan Abdirahman; Kakisim, Arzu Gorgulu

doi:10.1007/978-3-031-09753-9_17

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Opinion mining, also known as sentiment analysis, is a field of data mining concerned with analyzing people's opinions about entities, such as services or products [1]. It is an important topic of interest and an active field of study in all languages, especially for languages that lack sufficient scientific research and resources, such as datasets, lexicons, corpora, and tools [2]. One of these languages is the Arabic language, where the progress in Arabic opinion mining does not fit with the substantial Arab world population, contributing to generating enormous amounts of Arabic data on the web [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

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Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.

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