Sentiment Analysis and Opinion Mining Using Deep Learning for the Reviews on Google Play

Sari, Sercan; Kalender, Murat

doi:10.1007/978-3-030-66840-2_10

Cited by 4 publications

(8 citation statements)

References 15 publications

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“…In our experiment, we utilized the term frequency-inverse document frequency (TF-IDF) feature matrix, which showed the significance of terms in a review of the corpus [16]. Term frequency represents how often a given word appears within a text, while Inverse Document Frequency adjusts words that appear so many times in texts [50]. We utilized SciKit-Tfidf Learn's vectorizer Python package for this, which also ignored phrases with a document frequency more than or less than a minimum and maximum threshold, respectively [16].…”

Section: Features Extractionmentioning

confidence: 99%

“…Multinomial naïve Bayes (MNB): The naïve Bayes NB theorem is the foundation of the multinomial naïve Bayes classifier. Although NB is extremely efficient, its assumptions have an impact on the quality of the findings [50]. The multinominal naïve Bayes (MNB) approach was developed to overcome NB disadvantages.…”

Section: Sentiment Analysis Experiments Setupmentioning

confidence: 99%

“…MNB uses a multinomial model to represent the distribution of words in a corpus. MNB treats the text as if it were a series of words, assuming that the placement of words is independently determined of one another [50].…”

Section: Sentiment Analysis Experiments Setupmentioning

confidence: 99%

“…Decision tree (DT): One of the most often utilized approaches in classification is the decision tree algorithm. A DT's goal is to create a model that uses input data to predict the correct label for target variables [50]. It is a supervised method that builds a tree to hierarchically build a model.…”

Section: Sentiment Analysis Experiments Setupmentioning

confidence: 99%

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Sentiment Analysis of Emirati Dialect

Shamsi

Abdallah

2022

BDCC

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Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the Instagram platform. The constructed dataset consisted of more than 70,000 comments, mostly written in the Emirati dialect. We annotated the comments in the dataset based on text polarity, dividing them into positive, negative, and neutral categories, and the number of annotated comments was 70,000. Moreover, the dataset was also annotated for the dialect type, categorized into the Emirati dialect, Arabic dialects, and MSA. Preprocessing and TF-IDF features extraction approaches were applied to the constructed Emirati dataset to prepare the dataset for the sentiment analysis experiment and improve its classification performance. The sentiment analysis experiment was carried out on both balanced and unbalanced datasets using several machine learning classifiers. The evaluation metrics of the sentiment analysis experiments were accuracy, recall, precision, and f-measure. The results reported that the best accuracy result was 80.80%, and it was achieved when the ensemble model was applied for the sentiment classification of the unbalanced dataset.

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Section: Features Extractionmentioning

confidence: 99%

Section: Sentiment Analysis Experiments Setupmentioning

confidence: 99%

Section: Sentiment Analysis Experiments Setupmentioning

confidence: 99%

Section: Sentiment Analysis Experiments Setupmentioning

confidence: 99%

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Sentiment Analysis of Emirati Dialect

Shamsi

Abdallah

2022

BDCC

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“…Reviews of a product/service can be in the form of positive and negative reviews (Sari & Kalender, 2020). The increasing number of MyIndiHome application reviews on the Google Play site makes it difficult for companies to get comprehensive information from these reviews.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

Hakim

Putra

Khasanah

2021

Int. J. Ind. Optim.

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In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample. The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%. Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.

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Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

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Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users' reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users' reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users' opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.

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Sentiment Analysis and Opinion Mining Using Deep Learning for the Reviews on Google Play

Cited by 4 publications

References 15 publications

Sentiment Analysis of Emirati Dialect

Sentiment Analysis of Emirati Dialect

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

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