Related papers: Deep-Sentiment: Sentiment Analysis Using Ensemble …
Sentiment analysis or opinion mining help to illustrate the phrase NLP (Natural Language Processing). Sentiment analysis has been the most significant topic in recent years. The goal of this study is to solve the sentiment polarity…
Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms. Social media plays an essential role…
In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional…
Recent advances in Big Data has prompted health care practitioners to utilize the data available on social media to discern sentiment and emotions expression. Health Informatics and Clinical Analytics depend heavily on information gathered…
The early outcome prediction of ongoing or completed processes confers competitive advantage to organizations. The performance of classic machine learning and, more recently, deep learning techniques such as Long Short-Term Memory (LSTM) on…
Undoubtedly, social media are brainstormed by a tremendous volume of stories, feedback, reviews, and reactions expressed in various languages and idioms, even though some are factually incorrect. These motifs make assessing such data…
In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an advanced deep learning model based on Long Short-Term Memory (LSTM)…
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM). SAAM works on top of traditional neural networks and is designed to address the problem of multi-aspect sentiment classification and…
Sentiment analysis has been widely used to understand our views on social and political agendas or user experiences over a product. It is one of the cores and well-researched areas in NLP. However, for low-resource languages, like Bangla,…
We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings…
Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable,…
In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific…
Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection, aspect-category sentiment…
Multimodal sentiment analysis has attracted increasing attention with broad application prospects. The existing methods focuses on single modality, which fails to capture the social media content for multiple modalities. Moreover, in…
Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG).…
Sentiment analysis aims to extract people's emotions and opinion from their comments on the web. It widely used in businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Most of articles in this…
Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the…
We apply an ensemble pipeline composed of a character-level convolutional neural network (CNN) and a long short-term memory (LSTM) as a general tool for addressing a range of disinformation problems. We also demonstrate the ability to use…
With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social…
Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle…