Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation

Pre-research Study based on Bibliometrics, Deep Learning Research for Aspect-Based Sentiment Analysis

 Background: Massive publications on deep learning research for aspect-based sentiment analysis are challenging for interested researchers who want to research this area. Purpose: to provide an overview and comprehensive analysis on the research trend, which include the growth of publications, the most used keywords, the most popular publication sources to publish and find literature, the most cited publication, the most productive researcher, the most productive institution and country affiliation. Method: This study used a bibliometric method to analyze Scopus's indexed publications from 2014 (the year when the first publication was first indexed) to 2020. A total of 222 publications were analyzed and visualized using the VosViewer software. Result: In general, there is an increase in the number of publications from year to year. Keyword visualization shows keywords related to text-based processing, deep learning architectures, the research object and media, and the application of the method. The most popular sources to publish and to find literature are the “Lecture Notes in Computer Science” and the “Expert Systems with Applications''. The most cited publication is “Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review”, written by Do, Prasad (cited 81 times). The most productive researcher is Zhang Y from China. The most productive institution is Nanyang Technological University (6 publications), and China has the highest number of publications (76 documents). Conclusion: The bibliometric method can provide a conclusive and comprehensive preliminary overview of research trends for interested researchers who want to start research about deep learning for aspect-based sentiment analysis.   Keywords: Bibliometrics; Deep learning; Aspect-based sentiment analysis; VosViewer    Abstrak  Latar Balakang: Banyaknya publikasi mengenai penelitian deep learning untuk aspect-based sentiment analysis menjadi tantangan tersendiri bagi peneliti yang tertarik dan ingin memulai penelitian terkait topik ini. Tujuan: memberikan gambaran umum serta analisis komprehensif tren penelitian meliputi pertumbuhan jumlah publikasi, kata kunci yang banyak digunakan, sumber publikasi populer yang dapat dimanfaatkan untuk tujuan publikasi maupun menemukan literatur, publikasi utama yang paling banyak disitir, peneliti paling produktif dan pola kolaborasi peneliti, serta afiliasi institusi dan negara paling produktif. Metode: Kajian ini menggunakan metode bibliometrik untuk menganalisis publikasi terindeks Scopus dari tahun 2014 (tahun pertama kali publikasi terindeks) hingga tahun 2020. Sebanyak 222 judul publikasi dianalisis, kemudian divisualisasikan dengan software VosViewer. Hasil: Secara umum jumlah publikasi mengalami peningkatan dari tahun ke tahun. Visualisasi kata kunci menggambarkan kata kunci yang berkaitan dengan pemrosesan berbasis teks, arsitektur deep learning, obyek dan media penelitian, serta aplikasi aspect-based sentiment analysis dengan metode deep learning. Sumber publikasi terpopuler untuk tujuan publikasi dan sumber literatur utama berturut-turut adalah Lecture notes in Computer Science dan Expert Systems with Applications. Publikasi yang paling banyak disitir adalah Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review oleh Do, Prasad (disitir 81 kali). Peneliti paling produktif adalah Zhang Y dari Cina. Institusi yang paling produktif adalah Nanyang Technological University (6 publikasi), dan Cina menjadi negara paling produktif dengan jumlah publikasi sebanyak 76 dokumen. Kesimpulan: Kajian menggunakan metode bibliometrik dapat memberikan gambaran awal tren penelitian yang konklusif dan komprehensif bagi peneliti yang tertarik dan ingin memulai penelitian tentang topik deep learning untuk aspect-based sentiment analysis.   Kata kunci: Bibliometrika; Deep learning; Aspect-based sentiment analysis; VosViewer&nbsp

ATP: A holistic attention integrated approach to enhance ABSA

Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for inferring the sentiment polarity. These methods work well to capture the contextual relationship between the words of a review sentence. However, these methods are insignificant in capturing long-term dependencies. Attention mechanism plays a significant role by focusing only on the most crucial part of the sentence. In the case of ABSA, aspect position plays a vital role. Words near to aspect contribute more while determining the sentiment towards the aspect. Therefore, we propose a method that captures the position based information using dependency parsing tree and helps attention mechanism. Using this type of position information over a simple word-distance-based position enhances the deep learning model's performance. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA

Review Paper on Enhancing Stock Price Prediction with Sentiment Analysis of Bank Nifty Index

Sentiment analysis has emerged as a powerful tool in financial markets, offering the ability to harness market participants' collective wisdom and emotions to make more informed investment decisions. This research paper presents a comprehensive literature review on sentiment analysis models in predicting stock prices, focusing on the Bank Nifty Index, a critical benchmark in the Indian financial sector. The review begins by elucidating the significance of sentiment analysis in stock price prediction, highlighting its pivotal role in capturing market sentiment, investor emotions, and their impact on asset valuations. A detailed overview of sentiment sources, encompassing financial news, social media, reports, and other relevant data streams, provides insight into the rich tapestry of information that sentiment analysis can leverage. The paper delves into various sentiment analysis approaches, from traditional lexicon-based and rule-based methods to cutting-edge machine learning and deep learning techniques. It elucidates the intricate process of integrating sentiment analysis with stock price prediction models, exploring feature concatenation, time series integration, and weighted data to incorporate sentiment insights effectively. Evaluation metrics crucial for assessing the performance of both sentiment analysis models and stock price prediction models are thoroughly discussed. It concludes by highlighting the substantial potential of sentiment analysis in augmenting stock price prediction, offering investors and traders valuable tools to navigate the complexities of financial markets

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user's review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user's. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%

Sentiment analysis on movie reviews by recurrent neural networks and long short-term memory

Sentiment analysis has become important tool that can analyse review on any product or service that can be reviewed. Same goes to movie, all the audient are freely to make their own reviews on the movie that they watch and the reviews can be positive or negative based on audient satisfactions. Automated sentiment analysis is very important to make sure the analysis produce an accurate result and in faster time. By using the deep learning as the based to create the automated sentiment analysis it will be the great decision because of the deep learning structure that have multilevel of layer that can have sensitive process to classify the data. Upgrading the sentiment analysis using Recurrent Neural Networks (RNNs) and addition of Long Short-term Memory (LSTM) and also some modification on the number of layer with the mathematical calculation can improve the analysis accuracy. The dataset of the movie reviews will be collected on IMDB movie reviews database

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)