Sentiment analysis of the Saudi Digital Library (SDL) tweets interactions

In July 2011, the Saudi Digital Library (SDL) created a Twitter account to serve as a primary means for customer interaction, support, and a Q&A page. The SDL account actively tweets about SDL news, recently-added databases, and training venues, dates, and times. It is interesting to see SDL users interact with the SDL account on Twitter, but how beneficial is it? This study investigates the reactions of people who use the SDL to SDL tweets via Twitter, using a manual sentiment content analysis approach to analyze the interactions. The content analysis consists of counting the number of likes and retweets, whether the questions posted receive answers, and lastly categorizing the sentiment expressed in tweets as 'positive,' 'negative,' and 'neutral.' The students' interaction with SDL through Twitter ranges between positive and neutral. Students seem to like tweets about news and instructions about the SDL. However, students do not seem to find solutions to the problems they are having; instead, they are directed elsewhere to find help

Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis

We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work that was accepted for publication in Knowledge-Based System

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).

Sentiment Classification into Three Classes Applying Multinomial Bayes Algorithm, N-grams, and Thesaurus

The paper is devoted to development of the method that classi?es texts in English and Russian by sentiments into positive, negative, and neutral. The proposed method is based on the Multinomial Naive Bayes classi?er with additional n-grams application. The classi?er is trained either on three classes, or on two contrasting classes with a threshold to separate neutral texts. Experiments with texts on various topics showed signi?cant improvement of classification quality for reviews from a particular domain. Besides, the analysis of thesaurus relationships application to sentiment classification into three classes was done, however it did not show significant improvement of the classification results

What drives the helpfulness of online reviews? A deep learning study of sentiment analysis, pictorial content and reviewer expertise for mature destinations.

User-generated content (UGC) is a growing driver of destination choice. Drawing on dual-process theories on how individuals process information, this study focuses on the role of central and peripheral information processing routes in the formation of consumers’ perceptions of the helpfulness of online reviews. We carried out a two-step process to address the perceived helpfulness of user-generated content, a sentiment analysis using advanced machine-learning techniques (deep learning), and a regression analysis. We used a database of 2,023 comments posted on TripAdvisor about two iconic Venetian cultural attractions, St. Mark’s Square (an open, free attraction) and the Doge’s Palace (a museum which charges an entry fee). Following the application of deep-learning techniques, we first identified which factors influenced whether a review received a “helpful” vote by means of logistic regression. Second, we selected those reviews which received at least one helpful vote to identify, through linear regression, the significant determinants of TripAdvisor users’ voting behaviour. The results showed that reviewer expertise is an influential factor in both free and paid-for attractions, although the impact of central cues (sentiment polarity, subjectivity and pictorial content) is different in both attractions. Our study suggests that managers should look beyond individual ratings and focus on the sentiment analysis of online reviews, which are shown to be based on the nature of the attraction (free vs. paid-for)

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

MULDASA:Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialect