Meta heuristic approaches for sentiment analysis

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

Sentiment Analysis in Social Media and Its Application: Systematic Literature Review

Sentiment analysis or opinion mining used to capture the community's attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.

Manufacturers and brand owners apply sentiment analysis techniques on customer reviews to identify customer opinions on their products and services. Sentiment analysis at the document level or sentence level does not provide a complete view of the customer opinion because customers may express their opinion on different aspects of the product or service within a single review. This issue has inspired aspect-level opinion mining. Two core tasks are involved with aspect-level opinion mining: aspect detection and aspect-based sentiment analysis. This research is aimed at the first task - aspect detection. The focused domain is sportswear apparel, which has been largely overlooked in the field of opinion mining. Accordingly, this paper presents a new dataset produced with manual annotations by domain experts, according to a newly defined aspect taxonomy. This research compares the performance of a set of pre-trained language models for the considered task, and achieves state-of the-art performance for sportswear apparel reviews using a novel ensemble method.

Navigating Social Media in #Ophthalmology

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.

A Review on Reddit News Headlines with NLTK tool

With the recent emergence of Web-based applications and use of social networking sites, number of people are eager in expressing their views and opinions online. The sentimental analysis also referred to as opinion mining aims at processing user reviews (about products, movies, services, books, places, etc.). These reviews are often unstructured and need processing to evolve into the productive knowledge. Majority of the sentiment analysis works on the classification of opinion polarity with the use of simple classifiers. Handling diverse data distribution is one of the major issues that simple classifiers suffer. To cope up with the issue in this paper, we utilized the ensemble learners on the polarity prediction of the movie reviews. The proposed work processes the review data through some elementary steps that are conducted for the feature extraction in sentiment analysis. In addition to the feature extraction, we further perform the feature selection for the sake of dimensionality reduction. However, in contrast to the conventional simple learner, we applied the ensemble learner in the proposed model and evaluated its performance. To compare the ensemble model competence, we conducted the experiment on both individual as well as ensemble learner (random forest, AdaBoost, extra trees) and computed classification measures on both the model. IMDB dataset is used, and the polarity of a review, i.e., whether it is positive or negative, is predicted. With an extensive experimentation, it is found that results of ensemble classifiers are outperforming than individual learner in the classification of sentiment polarity.

A survey on deep learning based sentiment analysis

In the digital world, social media has become one of the most popular communication mediums that allow users to share their views on various topics in their social network. For example, Twitter users are allowed to share their thoughts on various topic by sending tweets with a maximum length of 140 characters. Hence, social media driven information contains opinions and sentiments on various topics of interest which are extremely useful for companies to design marketing strategies. Sentiment Analysis is widely used to assist people to understand the massive amount of data available online and identify the polarity of the topical based social media opinions. However, social media platforms’ users come from all over the world and have variation in terms of informal language and short notation used on social media platforms. Therefore, the identification on the polarity of topical social media has become more challenging and the accuracy on the polarity of topical social media opinions might be influenced. This paper investigates the effectiveness of applying different spelling correction algorithms, such as Levenshtein distance and Peter Norvig’s algorithm for spelling correction of unknown words found in social media such as Twitter, before carrying out sentiment analysis. The evaluation of spelling correction algorithms on sentiment analysis is carried out by comparing the polarities of manually annotated tweets with the polarities obtained from the sentiment analysis algorithm. Based on the results obtained, there are slight improvements in term of percentage of matched polarity, where 1.6% improvement by using the Levenshtein distance-based algorithm and 2.0% improvement by using the Peter Norvig’s algorithm.

Integrating color cues to improve multimodal sentiment analysis in social media

The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.

A Benchmark Dataset for Cricket Sentiment Analysis in Bangla Social Media Text

Social media are considered an excellent source of information and can provide opinions, thoughts and insights toward various important topics. Sentiment analysis becomes a hot topic in research due to its importance in making decisions based on opinions derived from analyzing the user’s contents on social media. Although the Arabic language is one of the widely spoken languages used for content sharing across the social media, the sentiment analysis on Arabic contents is limited due to several challenges including the morphological structures of the language, the varieties of dialects and the lack of the appropriate corpora. Hence, the rapid increase in research in Arabic sentiment analysis is grown slowly in contrast to other languages such as English. The contribution of this paper is twofold: First, we introduce a corpus of forty thousand labeled Arabic tweets spanning several topics. Second, we present three deep learning models, namely CNN, LSTM and RCNN, for Arabic sentiment analysis. With the help of word embedding, we validate the performance of the three models on the proposed corpus. The experimental results indicate that LSTM with an average accuracy of 81.31% outperforms CNN and RCNN. Also, applying data augmentation on the corpus increases LSTM accuracy by 8.3%.