754 research outputs found
Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
The essential use of natural language processing is to analyze the sentiment of the author
via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying
emotion in the context. It has been used in several subject areas such as stock market prediction, social
media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc.
Many researchers have worked on these areas and have produced significant results. These outcomes
are beneficial in their respective fields, as they help to understand the overall summary in a short
time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such
as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of
the essential studies done so far and to provide an overview of SA models in the area of emotion
AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA
along with machine learning models that are used to analyze the sentiment of the given context.
Furthermore, this work also discusses di erent neural network-based approaches for analyzing
sentiment. Finally, these di erent approaches were also analyzed with sample data collected from
Twitter. Among the four approaches considered in each domain, the aspect-based ontology method
produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85%
accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved
90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/
Sentiment analysis in SemEval: a review of sentiment identification approaches
ocial media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phasefostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions
Computational Sarcasm Analysis on Social Media: A Systematic Review
Sarcasm can be defined as saying or writing the opposite of what one truly
wants to express, usually to insult, irritate, or amuse someone. Because of the
obscure nature of sarcasm in textual data, detecting it is difficult and of
great interest to the sentiment analysis research community. Though the
research in sarcasm detection spans more than a decade, some significant
advancements have been made recently, including employing unsupervised
pre-trained transformers in multimodal environments and integrating context to
identify sarcasm. In this study, we aim to provide a brief overview of recent
advancements and trends in computational sarcasm research for the English
language. We describe relevant datasets, methodologies, trends, issues,
challenges, and tasks relating to sarcasm that are beyond detection. Our study
provides well-summarized tables of sarcasm datasets, sarcastic features and
their extraction methods, and performance analysis of various approaches which
can help researchers in related domains understand current state-of-the-art
practices in sarcasm detection.Comment: 50 pages, 3 tables, Submitted to 'Data Mining and Knowledge
Discovery' for possible publicatio
Sarcasm Detection on Text for Political Domain— An Explainable Approach
In the era of social media, a large volume of data is generated by applications such as the industrial internet of things, IoT, Facebook, Twitter, and individual usage. Artificial intelligence and big data tools plays an important role in devising mechanisms for handling this vast volume of data as per the required usage of data to form important information from this unstructured data. When the data is publicly available on the internet and social media, it is imperative to treat the data carefully to respect the sentiments of the individuals. In this paper, the authors have attempted to solve three problems for treating the data using AI and data science tools, weighted statistical methods, and explainability of sarcastic comments. The first objective of this research study is sarcasm detection, and the next objective is to apply it to a domain-specific political Reddit dataset. Moreover, the last is to predict sarcastic words using counterfactual explainability. The textare extracted from the self-annotated Reddit corpus dataset containing 533 million comments written in English language, where 1.3 million comments are sarcastic. The sarcasm detection based model uses a weighted average approach and deep learning models to extract information and provide the required output in terms of content classification. Identifying sarcasm from a sentence is very challenging when the sentence has content that flips the polarity of positive sentiment into negative sentiment. This cumbersome task can be achieved with artificial intelligenceand machine learningalgorithms that train the machine and assist in classifying the required content from the sentences to keep the social media posts acceptable to society. There should be a mechanism to determine the extent to which the model's prediction could be relied upon. Therefore, the explination of the prediction is essential. We studied the methods and developed a model for detecting sarcasm and explaining the prediction. Therefore, the sarcasm detection model with explainability assists in identifying the sarcasmfrom the reddit post and its sentiment score to classify given textcorrectly. The F1-score of 75.75% for sarcasm and 80% for the explainability model proves the robustness of the proposed model
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