A Review on Smart Bulb &amp; Proposed a Real Time Vision Based Smart Bulb using Image Processing

Mahto, Dashrath; Nair, Sreeja

doi:10.14445/22315381/ijett-v44p239

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Sentiment classification, widely called opinion mining, draws the attention of the customers using text mining and NLP approaches [16]; research has found that people potential, worker surveillance, and real-time insights [17] are all advantages of sentiment classification [18]. Network operators can use sentiment classification to find out what type of services they are missing and what areas of their existing customers are happy [19].…”

Section: Related Researchmentioning

confidence: 99%

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Mahto

Yadav

Lalotra³

2022

Mobile Information Systems

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With the emergence of social media platforms, most people have changed their way of interacting. Perhaps, sharing day-to-day lifestyle updates is a trend substantially influenced by microblogging sites, specifically Twitter, Facebook, Instagram, and many more. Moreover, text and messages are the most preferred way for such interactions. Twitter is one of the most commonly used microblogging tools that enable people to express their thoughts, opinions, emotions, happiness, sadness, excitement, ideas, mental stress, and so on. Hence, the sentiment prediction furnished by such textual data becomes a complex and challenging task. In this research, the authors proposed a hybridization of the convolutional neural network and bi-directional long short-term memory model (named ConvBidirectional-LSTM), which aims to better the categorization of sentiments of text data. Then, this proposed hybrid ConvBidirectional-LSTM model is compared with the existing state-of-the-art models, GloVe-based CNN-LSTM and Hierarchical Bi-LSTM (HeBiLSTM) models models. Furthermore, the performance of the proposed hybrid ConvBidirectional-LSTM model is evaluated on the US airline dataset using various performance parameters like accuracy, precision, recall, and f 1 score. The proposed model outperformed the existing state-of-the-art models with an accuracy rate of 93.25% in sentiment prediction.

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Section: Related Researchmentioning

confidence: 99%

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Mahto

Yadav

Lalotra³

2022

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

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“…Analysis of emotions during critical circumstances can be challenging since these situations possess high uncertainty and mixed feelings. Existing research works on the classification [11][12][13][14][15] of different sen-timents from short texts employ two techniques mainly: opinion lexicon and natural language processing (NLP) [16]. The majority of the semantic analysis approaches use the sentiment lexicon for recognizing emotional keywords for classification.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Mahto,

Yadav

2022

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Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F 1 -Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.

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A Review on Smart Bulb & Proposed a Real Time Vision Based Smart Bulb using Image Processing

Cited by 2 publications

References 1 publication

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Bulletin of the Polish Academy of Sciences: Technical Sciences

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