BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches (original) (raw)
Related papers
2021
Sentimental analysis is a context-based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine-learning techniques such as LSTM (Long short-term memory). This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python-based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM.
Sentiment analysis using global vector and long short-term memory
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Tweet sentiment analysis is a deep learning study that is beneficial for automatically determining public opinion on a certain topic. Using the long short-term memory (LSTM) algorithm, this paper aims to proposes a Twitter analysis technique that divides Tweets into two categories (positive and negative). The global vector (GloVe) word embedding score is used to rate many selected words as network input. GloVe converts words into vectors by building a corpus matrix. The GloVe outperforms its prior model, owing to its smaller vector and corpora sizes. GloVe has a higher accuracy than the model word embedding word2vec, continuous bag of word (CBoW), and word2vec Skip-gram. The preprocessed term variation was conducted to test the performance of sentiment classification. The test results show that this proposed method has succeeded in classifying with the best results with an accuracy of 95.61%.
Comparative Analysis of Deep Learning Approaches for Twitter Text Classification
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. Sentiment analysis aims to extract opinion automatically from data and classify them as positive and negative. Twitter widely used social media tools, been seen as an important source of information for acquiring people's attitudes, emotions, views, and feedbacks. Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion. In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis. Keras is a Deep Learning (DL) framework that provides an embedding layer to produce the vector representation of words present in the document. The objective of this work is to analyze the performance of deep learning models namely Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), bidirectional Long Short-Term Memory (Bi-LSTM), BERT and RoBERTa for classifying the twitter reviews. From the experiments conducted, it is found that RoBERTa model performs better than CNN and simple RNN for sentiment classification.
Natural Language Processing and Bi-Directional LSTM for Sentiment Analysis
International journal of computer applications, 2023
In e-commerce, one of the most critical and important aspects of the business model is customer reviews. Customer reviews reflect the satisfaction of customers with respect to the products and services offered. E-commerce is driven by significant amounts of data which poses a huge challenge of collection and evaluation to have an insight before decision-making and business strategy implementations. The field of natural language processing and machine learning techniques have provided significant leaps in helping the analysis of big data and business analytics. Also, Recurrent Neural Networks (RNN) evolved in so many powerful algorithms and one of those is the Bi-LSTM variation of RNNs. Bi-LSTM has been identified in the literature as a suitable machine learning classification algorithm for natural language processing due to its sequential learning process. This study is an implementation of the lemmatization natural language processing technique coupled with the Bi-LSTM machine learning classification technique for customer review sentiment analysis. The application of these two techniques has reported a significant performance accuracy in sentiment analysis of customer review data. The results in this study are reported as 96.06%, 91%, and 90% for accuracy, precision, and recall respectively.
A Novel Approach for Sentiment Analysis Using Deep Recurrent Networks and Sequence Modeling
Recent Patents on Engineering, 2019
Background: Due to the increasing growth of social websites, a lot of user-generated data is available these days in the form of customer reviews, opinions, and comments. Objective: Sentiment analysis includes analyzing the user reviews and finding the overall opinions from the reviews in terms of positive, negative and neutral categories. Sentiment analysis techniques can be used to assign a piece of text a single value that represents opinion expressed in that text. Sentiment analysis using lexicon approaches is already studied. Methods: A new approach to sentiment analysis using deep neural networks techniques is proposed. Deep neural networks using Sequence to sequence model is studied in this paper. The main objective of this paper is to identify the sequence of relationships among the words in the reviews. Customer reviews are taken from Amazon and sentiment analysis is done using the word embedding method. Results: The results obtained by the proposed method are compared with...
IJERT-Text based Sentiment Analysis using LSTM
International Journal of Engineering Research & Technology (IJERT), 2020
https://www.ijert.org/text-based-sentiment-analysis-using-lstm https://www.ijert.org/research/text-based-sentiment-analysis-using-lstm-IJERTV9IS050290.pdf Analyzing the big textual information manually is tougher and time-consuming. Sentiment analysis is a automated process that uses computing (AI) to spot positive and negative opinions from the text. Sentiment analysis is widely used for getting insights from social media comments, survey responses, and merchandise reviews to create data-driven decisions. Sentiment analysis systems are accustomed to add up to the unstructured text by automating business processes and saving hours of manual processing. In recent years, Deep Learning (DL) has garnered increasing attention within the industry and academic world for its high performance in various domains. Today, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are the foremost popular types of DL architectures used. We do sentiment analysis on text reviews by using Long Short-Term Memory (LSTM). Recently, thanks to their ability to handle large amounts of knowledge, neural networks have achieved a good success on sentiment classification. Especially long STM networks.
Hierarchical Long Short-Term Memory (LSTM) Model for News Sentiment Analysis
International Conference on Scientific and Innovative Studies, 2024
The study done on the use of the Hierarchical Long Short-Term Memory (LSTM) model for news sentiment analysis is succinctly summarised in this abstract. The purpose of the study is to find out how well LSTM captures the subtleties of sentiment seen in news stories. There is a wealth of textual data available that may be analysed to understand public opinion thanks to the growth of social media and online news sources. The intricacy of sentiment represented in news items is frequently too complicated for traditional sentiment analysis techniques to fully capture. An appropriate choice for sentiment analysis is the LSTM model, a kind of recurrent neural network that has demonstrated promise in recognising sequential input and capturing long-term relationships. An extensive dataset of news stories from reliable sources was gathered, and human annotators assigned sentiment labels to each one in order to assess the LSTM model's performance. Preprocessing the data involved translating the text into a numerical representation and eliminating stopwords in order to get it ready for LSTM training. To increase the LSTM model's effectiveness in sentiment analysis, more study may look at adding attention mechanisms or transfer learning strategies. In summary, the present study showcases the efficacy of the LSTM model in assessing sentiment inside news stories and highlights its potential for wider use in comprehending public opinion.
Word Embedding, Neural Networks and Text Classification: What is the State-of-the-Art?
2019
In this bachelor thesis, I first introduce the machine learning methodology of text classification with the goal to describe the functioning of neural networks. Then, I identify and discuss the current development of Convolutional Neural Networks and Recurrent Neural Networks from a text classification perspective and compare both models. Furthermore, I introduce different techniques used to translate textual information in a language comprehensible by the computer, which ultimately serve as inputs for the models previously discussed. From there, I propose a method for the models to cope with words absent from a training corpus. This first part has also the goal to facilitate the access to the machine learning world to a broader audience than computer science students and experts. To test the proposal, I implement and compare two state-of-the-art models and eight different word representations using pre-trained vectors on a dataset given by LogMeIn and on a common benchmark. I find ...
WRS: A Novel Word-embedding Method for Real-time Sentiment with Integrated LSTM-CNN Model
Artificial Intelligence (AI) is a research-focused technology in which Natural Language Processing (NLP) is a core technology in AI. Sentiment Analysis (SA) aims to extract and classify the people's opinions by NLP. The Machine Learning (ML) and lexicon dictionaries have limited competency to efficiently analyze massive live media data. Recently, deep learning methods significantly enrich the accuracy of recent sentiment models. However, the existing methods provide the aspect-based extraction that reduces individual word accuracy if a sentence does not follow the aspect information in real-time. Therefore, this paper proposes a novel word embedding method for the real-time sentiment (WRS) for word representation. The WRS's novelty is a novel word embedding method, namely, Word-to-Word Graph (W2WG) embedding that utilizes the Word2Vec approach. The WRS method assembles the different lexicon resources to employ the W2WG embedding method to achieve the word feature vector. Robust neural networks leverage these features by integrating LSTM and CNN to improve sentiment classification performance. LSTM is utilized to store the word sequence information for the effective real-time SA, and CNN is applied to extract the leading text features for sentiment classification. The experiments are conducted on Twitter and IMDB datasets. The results demonstrate our proposed method's effectiveness for real-time sentiment classification.