Comparative Study of Deep Learning-Based Sentiment Classification

Seo, Seungwan; Kim, Czangyeob; Kim, Hae-Dong; Mo, Kyounghyun; Kang, Pilsung

doi:10.1109/access.2019.2963426

Cited by 71 publications

(34 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed methodology is two-fold process wherein the first part analyzes the tweets of the user using an ensemble [9] and deep learning classifier [10] to predict the political party of a user. This is supplemented with the analysis of tweets and sarcastic contents, if present.…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

Twitter is one of the most popular social networking platforms of today’s generation and is a fundamental tool in harvesting the data of many users worldwide. Many discussions ranging from current affairs, news sharing, filing complaints to advertising and discussing some common interests etc. happen on Twitter. It is widely used by many famous politicians as a prominent communication medium to address large masses ,owing to its mass usage and popularity . Thus, it is safe to assume that people communicate their political ideologies on Twitter. Many works have been done so far to deduce the stance of user towards a particular party by performing sentiment analysis on their tweets using popular classifiers. To find connected and similar users, earlier works generated a social network graph, based on the assumption that friends and followers share similar interests, which might not be true in all cases. In contrast, the proposed work employs the concept of ensemble classifier (a single classifier generated from several base learning classifiers) to analyze the tweets and makes use of multiple interaction elements like followers/ following, mentions, re-tweets ,hash tags etc. to infer which political party a user identifies with. These interaction elements project out the homophily (users who share same beliefs and choices) amongst the users. The proposed study can be encompassed to any domain and can be used by advertising agencies, marketing companies, e-commerce, heath care etc. to identify their target audience.

show abstract

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Symmetrically in branch B, the features are extracted based on word representation methods. Since RNNs provide better performance on word embedding vectors [37], we use BiL-STM and word embedding together in the study. Following the same approach, CNN and character-level embedding used together [23].…”

Section: ) Model Baselinesmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Sentiment Classification

Salur¹,

Aydın²

2020

IEEE Access

130

View full text Add to dashboard Cite

A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services. However, sentiment classification plays an important role in the user's feedback evaluation. At present, deep learning such as long short-term memory (LSTM), gated recurrent unit (GRU), bidirectionally long short-term memory (BiLSTM) or convolutional neural network (CNN) are prevalently preferred in sentiment classification. Moreover, word embedding such as Word2Vec and FastText is closely examined in text for mapping closely related to the vectors of real numbers. However, both deep learning and word embedding methods have strengths and weaknesses. Combining the strengths of the deep learning models with that of word embedding is the key to high-performance sentiment classification in the field of natural language processing (NLP). In the present study, we propose a novel hybrid deep learning model that strategically combines different word embedding (Word2Vec, FastText, character-level embedding) with different deep learning methods (LSTM, GRU, BiLSTM, CNN). The proposed model extracts features of different deep learning methods of word embedding, combines these features and classifies texts in terms of sentiment. To verify the performance of the proposed model, several deep learning models called basic models were created to perform series of experiments. By comparing, the performance of the proposed model with that of past studies, the proposed model offers better sentiment classification performance. INDEX TERMS Sentiment classification, Turkish tweets analysis, hybrid model, word embedding, deep learning, LSTM, CNN.

show abstract

“…Independently, there exists several works in literature focused on developing supervised and unsupervised models for understanding sentiment from user utterance [ 32 – 34 ]. However, there exists very little work that utilizes these additional information of the user behavior in the decision making process for the VA to be efficient and competent enough to converse and execute its goal appropriately.…”

Section: Related Workmentioning

confidence: 99%

Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning

Saha

Bhattacharyya

2020

PLoS ONE

View full text Add to dashboard Cite

Developing a Dialogue/Virtual Agent (VA) that can handle complex tasks (need) of the user pertaining to multiple intents of a domain is challenging as it requires the agent to simultaneously deal with multiple subtasks. However, majority of these end-to-end dialogue systems incorporate only user semantics as inputs in the learning process and ignore other useful user behavior and information. Sentiment of the user at the time of conversation plays an important role in securing maximum user gratification. So, incorporating sentiment of the user during the policy learning becomes even more crucial, more so when serving composite tasks of the user. Methodology As a first step towards enabling the development of sentiment aided VA for multi-intent conversations, this paper proposes a new dataset, annotated with its corresponding intents, slot and sentiment (considering the entire dialogue history) labels, named SentiVA, collected from open-sourced dialogue datasets. In order to integrate these multiple aspects, a Hierarchical Reinforcement Learning (HRL) specifically options based VA is proposed to learn strategies for managing multi-intent conversations. Along with task success based immediate rewards, sentiment based immediate rewards are also incorporated in the hierarchical value functions to make the VA user adaptive. Findings Empirically, the paper shows that task based and sentiment based immediate rewards cumulatively are required to ensure successful task completion and attain maximum user satisfaction in a multi-intent scenario instead of any of these rewards alone. Practical implications The eventual evaluators and consumers of dialogue systems are users. Thus, to ensure a fulfilling conversational experience involving maximum user satisfaction requires VA to consider user sentiment at every time-step in its decision making policy.

show abstract

Comparative Study of Deep Learning-Based Sentiment Classification

Cited by 71 publications

References 57 publications

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

A Novel Hybrid Deep Learning Model for Sentiment Classification

Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning

Contact Info

Product

Resources

About