Sentiment analysis algorithms: evaluation performance of the Arabic and English language

Abo, Mohamed Elhag Mohamed; Shah, Nor Shahida Mohd; Balakrishnan, Vimala; Abdelaziz, Ahmed

doi:10.1109/iccceee.2018.8515844

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…For the Naive Bayes and RNN-algorithm, the accuracies were 81% and 87% for the IMDB dataset, 69% and 88% for the IMDB dataset, 60% and 93% for the Airline dataset respectively. Also, the resulting experiment done in paper [15] showed the Naive Bayes result is 96.6%, which is higher than other classifiers, Nearest Neighbor (k-NN) and support vector machine (SVM). So, in our experiment, we have chosen these two methods i.e.…”

Section: Introductionmentioning

confidence: 85%

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Aryal¹,

Bhattarai²

2021

Adv. Engin. & Technol.

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Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

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Section: Introductionmentioning

confidence: 85%

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Aryal¹,

Bhattarai²

2021

Adv. Engin. & Technol.

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“…Similarly, another study [33] used NB classifier to classify the sentiment polarity for Arabic and English languages as it is considered the most effective performer in data mining applications. According to other research [30], [34], evidence in literature suggests the effective performance of NB classifiers in classification tasks in general and in sentiment classification in particular. However, Multinomial Naïve Bayes (MNB) and complement Naïve Bayes (CNB) are probabilistic models for NB classification in which MNB is a unigram language model that captures the information of words counts in a document and it performs well with a large vocabulary size.…”

Section: ) the Initiative Relevance Classification Modelmentioning

confidence: 88%

Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

et al. 2020

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Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.

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“…The algorithm agreed 83% of the time with human coders on the appropriate sentiment (positive (pos) or negative (neg)) of each piece of text in the testing set. This is typical performance for sentiment analysis algorithms [38], which range from the high 50s to the low 90s in terms of accuracy percentage. The human coders themselves only agreed 85% of the time and had to negotiate "correct" classification for the remaining 15% of cases.…”

Section: Sl Estimation: Slacda Vs Manual Codingmentioning

confidence: 97%

A Natural Language Processing Approach to Social License Management

Boutilier¹,

Bahr

2020

Sustainability

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Dealing with the social and political impacts of large complex projects requires monitoring and responding to concerns from an ever-evolving network of stakeholders. This paper describes the use of text analysis algorithms to identify stakeholders’ concerns across the project life cycle. The social license (SL) concept has been used to monitor the level of social acceptance of a project. That acceptance can be assessed from the texts produced by stakeholders on sources ranging from social media to personal interviews. The same texts also contain information on the substance of stakeholders’ concerns. Until recently, extracting that information necessitated manual coding by humans, which is a method that takes too long to be useful in time-sensitive projects. Using natural language processing algorithms, we designed a program that assesses the SL level and identifies stakeholders’ concerns in a few hours. To validate the program, we compared it to human coding of interview texts from a Bolivian mining project from 2009 to 2018. The program’s estimation of the annual average SL was significantly correlated with rating scale measures. The topics of concern identified by the program matched the most mentioned categories defined by human coders and identified the same temporal trends.

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Sentiment analysis algorithms: evaluation performance of the Arabic and English language

Cited by 19 publications

References 15 publications

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

A Natural Language Processing Approach to Social License Management

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