Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2011, Advances in Distributed Agent-Based Retrieval …
We argue in this paper that in order to properly capture opinion and sentiment expressed in texts or dialogs any system needs a deep linguistic processing approach. As in other systems, we used ontology matching and concept search, based on standard lexical resources, but a natural language understanding system is still required to spot fundamental and pervasive linguistic phenomena. We implemented these additions to VENSES system and the results of the evaluation are compared to those reported in the state-of-the-art systems in sentiment analysis and opinion mining. We also provide a critical review of the current benchmark datasets as we realized that very often sentiment and opinion is not properly modeled.
Computers, Materials & Continua
Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced. Further, a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions. Novelty of DSCNet model lies in introducing the deep contextual. DSCNet is evaluated on three datasets i.e., Restaurant, Laptop, and Twitter dataset considering different deep learning (DL) metrics like precision, recall, accuracy, and Macro-F1 score. Also, comparative analysis is carried out with different baseline methods in terms of accuracy and Macro-F1 score. DSCNet achieves 92.59% of accuracy on restaurant dataset, 86.99% of accuracy on laptop dataset and 78.76% of accuracy on Twitter dataset.
International Journal of Informatics and Communication Technology (IJ-ICT)
When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user's likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don't have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) or opinion mining (OM) is an algorithmic method for automatically determining the polarity of comments and reviews based on their content. Emotional intelligence relies on text categorization to work. In the age of big data, there are countless ways to use sentiment analysis, yet SA remains a challenge. As a result of its enormous importance, sentiment analysis is a hotly debated topic in the commer...
2018
The opinions of people and others are one of the main influencers of human behaviour and activities. Therefore, individuals and organizations often consult with others to understand their opinions or attitudes towards a certain topic, before making decisions. Also, for telecommunication enterprises to survive, they need to be attentive to their customers’ opinions. Sentiment analysis is a technique that is often used by organizations to categorize and understand the underlying attitude of a person towards an entity, product, topic, etc. Though it has been traditionally performed using text-based sources, it has been suggested that other modalities should be explored. One such alternative to text-based sources is video recordings of people using or reviewing content. Videos can contain multiple modals including text, voice, and facial expressions, which can be used to detect a person’s attitude towards a topic. An approach to performing sentiment analysis using affective computing fo...
Advances in Science, Technology and Engineering Systems Journal
Presently, individuals generate tremendous volumes of information on the internet. As a result, sentiment analysis is a critical tool for automating a deep understanding of usergenerated information. Of late, deep learning algorithms have shown endless promises for a variety of sentiment analysis. The purpose of sentiment analysis is to categorize different descriptions as good, bad, or impartial based on context data. Numerous studies have been concentrated on sentiment analysis in addition to the ability to examine thoughts, views, and reactions. In this paper, we review classical and deep learning approaches that have been applied to various sentiment analysis tasks and their evolution over last years and provide performance analysis of different sentiment analysis models on particular datasets. In the end, we will highlight current challenges and suggested solutions that can be considered in future work to achieve better performance.
2019
Deep learning has detonated in the public responsiveness, primarily as predictive and analytical products pervade our world, in the form of innumerable humancentered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and mock-up self-driving vehicle systems. In contrast, researchers across disciplines have been including into their research to solve various natural language processing issues. In this paper we seek to provide a thorough exploration of Deep learning and its applications like sentimental analysis and natural language processing (NLP). Deep learning has an edge over the traditional machine learning algorithms, like support vector machine (SVM) and Naïve Bayes, for sentiment analysis because of its potential to overcome the challenges faced by sentiment analysis and handle the diversities involved, without the expensive demand for manual feature engineering. Deep learning models promise one thing given sufficient amount ...
The International FLAIRS Conference Proceedings
Sentiment classification (SC) is an important natural language processing (NLP) task that aims to determine the sentiment or emotional tone in a given text. With the increasing pervasiveness of internet-based applications and social media, massive amounts of unstructured data are generated daily, elevating the opportunity and challenges associated with automated sentiment extraction for tasks such as customer feedback analysis, social media monitoring, and opinion mining. In this review paper, we provide an update on the state of the art in sentiment analysis, including an overview of and classification methods leveraging machine learning and deep learning methods.
ICST transactions on scalable information systems, 2024
Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.
2011
This paper presents a lexicon model for subjectivity description of Dutch verbs that offers a framework for the development of sentiment analysis and opinion mining applications based on a deep syntactic-semantic approach. The model aims to describe the detailed subjectivity relations that exist between the participants of the verbs, expressing multiple attitudes for each verb sense. Validation is provided by an annotation study that shows that these subtle subjectivity relations are reliably identifiable by human annotators.
International Journal of Advanced Computer Science and Applications
The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.
2023 Computer Applications & Technological Solutions (CATS), 2024
Social media plays a vital role in our daily lives. To understand and interpret emotions and opinions expressed on social media platforms, analyzing sentiment is very important. Our study is based on Twitter sentiment analysis. Our aim is to classify tweets automatically as positive, negative, or neutral based on their content using natural language processing and machine learning algorithms. The dataset we used for our analysis is extracted from the website called mendeley data and also we have added some tweets manually which covers various topics. To remove noise, including URLs, hashtags, punctuations, and user mentions, and to retain essential textual content and emojis, we pre-processed the dataset. Additionally, for our research, we used VADER (Valence Aware Dictionary and sentiment Reasoner) and Transformers-RoBERTa to analyze the sentiment of various tweets. We evaluate the performance of these two models using evaluation metrics such as accuracy, precision, recall and F1-score, and also confusion metrics on the testing set. We also discuss the study's limitations and conclude that machine learning-based sentiment analysis models are a reliable tool for the sentiment analysis of the twitter dataset.
Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments expressed for certain products and services embeds complex processes and pose research challenges, due to the textual phenomena and the language specific expression variations. This paper proposes a fast, flexible, generic methodology for sentiment detection out of textual snippets which express people’s opinions in different languages. The proposed methodology adopts a machine learning approach with which textual documents are represented by vectors and are used for training a polarity classification model. Several documents’ vector representation approaches have been studied, including lexicon-based, word embedding-based and hybrid vectorizations. The competence of these feature representations for the sentiment classification task is assessed through experiments on four datasets containing online user reviews in both Greek and English languages, in order to represent high and weak inflection language groups. The proposed methodology requires minimal computational resources, thus, it might have impact in real world scenarios where limited resources is the case.
Electronics, 2020
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.
Multidiszciplináris tudományok, 2022
Getting the context out of the text is the main objective of sentiment analysis. Today's digital world provides us with many data raw forms: Twitter, Facebook, blogs, etc. Researchers need to convert this raw data into useful information for performing analysis. Many researchers devoted their precious time to get the text's polarity using deep learning and conventional machine learning methods. In this paper, we reviewed both the approaches to gain insight into the work done. This paper will help the researchers to choose the best methods for classifying the text. We pick some of the best articles and critically analyze them in different parameters like dataset used, feature extraction technique, accuracy, and resource utilization.
IRJET, 2021
In recent years, internet strengthened itself as an influential platform that has changed the methods of business and communication. Sentiment Analysis (SA) become active research topic in the field of Natural Language Processing (NLP). It is the discipline that analyzes sentiment of people's opinions, attitude and emotions towards different entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. The growth of web and social media such as blogs, business reviews, and social networks have fueled awareness in Sentiment Analysis (SA). There are several methods to analyze sentiments and all methods have numerous challenges, lacks and limitations. Therefore, this area still demands attention of researchers as well as industrialists. The main objective of this survey is to demonstrate an overview of Sentiment Analysis (SA) techniques and highlights limitations from previous studies. Our contribution will provide quick review of latest papers and help the researchers to choose appropriate for their future work. Finally, we attempt to compare the approaches and conclude that which approach can provide high accuracy and highlights more useful algorithms for the SA.
Computers, Materials & Continua
In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users' emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users' emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
We present and evaluate several hybrid systems for sentiment identification for Twitter, both at the phrase and document (tweet) level. Our approach has been to use a novel combination of lexica, traditional NLP and deep learning features. We also analyse techniques based on syntactic parsing and tokenbased association to handle topic specific sentiment in subtask C. Our strategy has been to identify subphrases relevant to the designated topic/target and assign sentiment according to our subtask A classifier. Our submitted subtask A classifier ranked fourth in the Se-mEval official results while our BASELINE and µPARSE classifiers for subtask C would have ranked second.
ArXiv, 2018
In today's scenario, imagining a world without negativity is something very unrealistic, as bad NEWS spreads more virally than good ones. Though it seems impractical in real life, this could be implemented by building a system using Machine Learning and Natural Language Processing techniques in identifying the news datum with negative shade and filter them by taking only the news with positive shade (good news) to the end user. In this work, around two lakhs datum have been trained and tested using a combination of rule-based and data driven approaches. VADER along with a filtration method has been used as an annotating tool followed by statistical Machine Learning approach that have used Document Term Matrix (representation) and Support Vector Machine (classification). Deep Learning algorithms then came into picture to make this system reliable (Doc2Vec) which finally ended up with Convolutional Neural Network(CNN) that yielded better results than the other experimented modules...
2024 19th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP), 2024
Human emotions are often discerned through tone, facial expressions, and gestures via face-to-face interactions. However, the question arises: Can sentiment be accurately identified from unstructured text on social networks? In this study, we demonstrate that it is indeed possible. We applied four machine learning methods—Deep Learning, Decision Trees, Naive Bayes, and Support Vector Machines—in two classification scenarios: cross-validation and training/test sets enhanced by a meta-classifier. Our goal was to identify which combination of classification scenario, learning method, and preprocessing performs best in sentiment analysis. To validate our approach, we used a manually labeled corpus, forming three datasets of different sizes with varying preprocessing techniques. The results underscore the viability and effectiveness of the proposed approach and provide implications for various fields (product development, marketing, political analysis, customer service education, linguistic education).
Journal of ICT Standardization
Sentiment classification provides a means of analysing the subjective information in the text and subsequently extracting the opinion. Sentiment analysis is the method by which people extract information from their opinions, judgments and emotions about entities. In this paper we propose a comparative study between the most deep learning models used in the field of sentiment analysis; L-NFS (Linguistique Neuro Fuzzy System), GRU (Gated Recurrent Unit), BiGRU (Bidirectional Gated Recurrent Unit), LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory) and BERT(Bidirectional Encoder Representation from Transformers), we used for this study a large Corpus contain 1.6 Million tweets, as devices we train our models with GPU (graphics processing unit) processor. As result we obtain the best Accuracy and F1-Score respectively 87.36% and 0.87 for the BERT Model.
Applied Sciences, 2019
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 different 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 offers 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 different neural network-based approaches for analyzing sentiment. Finally, these different 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.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.