An expandable Arabic lexicon and valence shifter rules for sentiment analysis on twitter

Sentiment analysis (SA) refers as computational and natural language processing techniques used to extract subjective information expressed in a text. In this SA study, three main problems are addressed: a) absence of resources on Palestinian Arabic dialect (PAL), b) emergence of new sentiment words, hence decreases the performance of sentiment analysis models when applied on tweets collected, and c) handling valence shifter words were not thoroughly addressed in Arabic sentiment analysis. Therefore, this study aims to construct a PAL lexicon for Palestinian tweets and to design an Expandable and Up-to-date Lexicon for Arabic (EULA). A new valence shifter rules in enhancing the performance of lexicon-based sentiment analysis on Arabic tweets is also been constructed. In this study, a PAL lexicon is built by using phonology matching algorithm while EULA is constructed by harnessing a general lexicon on a tweets dataset to find new terms and predict its polarity through some linguistic rules. Furthermore, a set of rules are proposed to handle the valence shifters words by applying rules to find the scope of words, and shifting value that is produced by these words. Palestinian and Arabic tweets datasets from March to May 2018 are used to evaluate the proposed idea. Experimental results indicate that the proposed PAL lexicon has produced better results compared to other lexicons when tested on Palestinian dataset. Meanwhile, EULA enhanced the performance of lexicon-based approach to be competitive with machine learning approach. Moreover, applying the proposed valence shifter rules have increased overall performance of 5% on average. The new proposed PAL sentiment lexicon is able to handle Palestinian’s dialects. Furthermore, the EULA has overcome the emergence of new slang words in social media. Moreover, the constructed valence shifter rules are capable to handle negation, intensifiers and contrasts in enhancing the performance of Arabic sentiment analysis

Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach

Despite the enormous increase in the number of Arabic posts on social networks, the sentiment analysis research into extracting opinions from these posts lags behind that for the English language. This is largely attributed to the challenges in processing the morphologically complex Arabic natural language and the scarcity of Arabic NLP tools and resources. This complex task is further exacerbated when analysing dialectal Arabic that do not abide by the formal grammatical structure. Based on the semantic modelling of the target domain’s knowledge and multi-factor lexicon-based sentiment analysis, the intent of this research is to use a hybrid approach, integrating linguistic and machine learning methods for sentiment analysis classification of dialectal Arabic. First, a dataset of dialectal Arabic tweets was collected focusing on the unemployment domain, which is annotated manually. The tweets cover different dialectal Arabic in Saudi Arabia for which a comprehensive Arabic sentiment lexicon was constructed. This approach to sentiment analysis also integrated a novel light stemming mechanism towards improved Saudi dialectal Arabic stemming. Subsequently, a novel multi-factor lexicon-based sentiment analysis algorithm was developed for domain-specific social media posts written in dialectal Arabic. The algorithm considers several factors (emoji, intensifiers, negations, supplications) to improve the accuracy of the classifications. Applying this model to a central problem of sentiment analysis in dialectical Arabic, these operational techniques were deployed in order to assess analytical performance across social media channels which are vulnerable to semantic and colloquial variations. Finally, this study presented a new hybrid approach to sentiment analysis where domain knowledge is utilised in two methods to combine computational linguistics and machine learning; the first method integrates the problem domain semantic knowledgebase in the machine learning training features set, while the second uses the outcome of the lexicon-based sentiment classification in the training of the machine learning methods. By integrating these techniques into a single, hybridised solution, a greater degree of accuracy and consistency was achieved than applying each approach independently, confirming a pragmatic solution to sentiment classification in dialectical Arabic text

Exploring Sentiment Analysis on Arabic Tweets about the COIVD-19 Vaccines

The COVID-19 pandemic has imposed a public health crisis across the world. The global efforts lead to the development and deployment of multiple vaccines. The success of ending the COVID-19 pandemic relies on the willingness of people to get the vaccines. Social media platforms prove to be a valuable source to perform experiments on sentiment and emotion towards COVID-19 vaccination in many languages, mainly focusing on English. The people express their opinions and emotion on Twitter briefly, which can have tracked almost instantaneously. This helps the governments, public health officials, and decision-makers to understand public opinions towards vaccines. The goal of this research is to investigate public sentiment on COVID-19 vaccines. Twitter social media extracted all Arabic-language tweets mentioning seven vaccines in 7 months from 1 November 2020 to 31 May 2021. A set of Arabic sentiment lexicons were prepared to perform the sentiment analysis. The tweets\u27 monthly average sentiment were calculated from the collected dataset and evaluated comparatively for each vaccine throughout the 11 months. Out of 5.5 million tweets that have been retrieved using the most frequent keywords and hashtags during the COVID-19 pandemic, 202,427 tweets were only considered and included in the monthly sentiment analysis. We considered tweets that mentioned only one vaccine name of the text. The distribution of tweets shows that 47.5% of the considered tweets mentioned the Pfizer vaccine. It is reported that 64% of the total tweets are non-negative while 35% are negative, with a significant difference in sentiment between the months. We observed an increase of non-negative tweets in parallel with increasing negative tweets on May 2021, reflecting the public\u27s rising confidence towards vaccines. Lexicon-based sentiment analysis is valuable and easy to implement the technique. It can be used to track the sentiment regarding COVID-19 vaccines. The analysis of social media data benefits public health authorities by monitoring public opinions, addressing the people\u27s concerns about vaccines, and building the confidence of individuals towards vaccines