Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Poria, Soujanya; Hazarika, Devamanyu; Majumder, Navonil; Mihalcea, Rada

doi:10.48550/arxiv.2005.00357

Cited by 9 publications

(9 citation statements)

References 113 publications

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“…Recent developments in NLP have produced effective ways for automatic sentiment analysis using ML algorithms [50]. Lexicon-based approaches using supervised and unsupervised ML algorithms, including Nai"ve Bayes and Support Vector Machines (SVM) , have generated high accuracy in sentiment cla ssification.…”

Section: Related Opinion Mining Approachesmentioning

confidence: 99%

“…Although neural network models have established higher accuracy with their automated feature learning , the application of such models is often limited by their heavy reliance on manually annotated data for trainin g language models [50]. Within neural network models , considerable progress has been achieved by the models using the Transformer architecture, which is based on an self-attention mechanism [65].…”

Section: Related Opinion Mining Approachesmentioning

confidence: 99%

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Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

et al. 2021

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Public acceptance and support for renewable energy are important determinants of the low-carbon energy transition. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform on which people post messages, known as tweets. We filtered tweets specific to solar energy and performed a classification task using Robustly optimized Bidirectional Encoder Representations from Transformers (RoBERTa). Our RoBERTa-based sentiment classification model, fine-tuned with 6300 manually annotated tweets specific to solar energy, attains 80.2% accuracy for ternary (positive, neutral, or negative) classification. Analyzing 266,686 tweets during the period of January to December 2020, we find public sentiment varies widely across states (Coefficient of Variation =164.66%). Within the study period, the Northeast U.S. region shows more positive sentiment toward solar energy than did the South U.S. region. Public opinion on solar energy is more positive in states with a larger share of Democratic voters in the 2020 presidential election. Public sentiment toward solar energy is more positive in states with consumer-friendly net metering policies and a more mature solar market. States that wish to gain public support for solar energy might want to consider implementing consumer-friendly net metering policies and support the growth of solar businesses.

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Section: Related Opinion Mining Approachesmentioning

confidence: 99%

Section: Related Opinion Mining Approachesmentioning

confidence: 99%

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

et al. 2021

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“…With the abundance of user-generated online content, such as videos, Multimodal Sentiment Analysis (MSA) of human spoken language has become an important area of research [33,45]. Unlike traditional affect learning tasks performed on isolated modalities (such as text, speech), multimodal learning leverages multiple sources Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

Hazarika¹,

Zimmermann²,

Poria³

2020

Preprint

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Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of usergenerated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modalityinvariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

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“…The inadequacy of benchmark dataset and the limited amount of etextual contents or reviews in the Bengali language resulted in the sentiment classification task complicated. Deep learning algorithms are very effective to tackle such complications and classify the sentiments correctly [1,19]. One main advantage of these algorithms are their ability to capture the semantic information in long texts.…”

Section: Introductionmentioning

confidence: 99%

SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews

Hossain¹,

Sharif²,

Hoque³

et al. 2020

Preprint

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The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of the people express their opinion, emotion or sentiment about any product or service in the form of tweets, Facebook post or status, blog write up, and reviews. Sentiment analysis deals with the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude toward a particular topic is positive, negative, or neutral. The impact of customer review is significant to perceive the customer attitude towards a restaurant. Thus, the automatic detection of sentiment from reviews is advantageous for the restaurant owners, or service providers and customers to make their decisions or services more satisfactory. This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the reviews provided by the clients of the restaurant into positive and negative polarities. A corpus consists of 8435 reviews is constructed to evaluate the proposed technique. In addition, a comparative analysis of the proposed technique with other machine learning algorithms presented. The results of the evaluation on test dataset show that BiLSTM technique produced in the highest accuracy of 91.35%.

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Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Cited by 9 publications

References 113 publications

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews

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