deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks
            for Sentiment Analysis in Twitter

Yang, Tzu-Hsuan; Tseng, Tzu-Hsuan; Chen, Chia-Ping

doi:10.18653/v1/s17-2101

Cited by 6 publications

(4 citation statements)

References 7 publications

(12 reference statements)

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“…Tzu-Hsuan Yang et al, implemented [7] a system for SA task in twitter dataset. This system is a combination of two deep neural networks based models such as LSTM Recurrent Neural Network and convolution neural network (CNN) through interpolation.…”

Section: Related Workmentioning

confidence: 99%

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Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

Jasti¹,

Kumar²

2022

IJACSA

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In the world of social media, the amount of textual data is increasing exponentially on the internet, and a large portion of it expresses subjective opinions. Sentiment Analysis (SA) also named as Opinion mining, which is used to automatically identify and extract the subjective sentiments from text. In recent years, the research on sentiment analysis started taking off because of a huge of amount of data is available on the social media like twitter, machine learning algorithms popularity is increased in IR (Information Retrieval) and NLP (Natural Language Processing). In this work, we proposed three phase systems for sentiment classification in twitter tweets task of SemEval competition. The task is predicting the sentiment like negative, positive or neutral of a twitter tweets by analyzing the whole tweet. The first system used Artificial Bee Colony (ABC) optimization technique is used with Bag-of-words (BoW) technique in association with Naive Bayes (NB) and k-Nearest Neighbor (kNN) classification techniques with combination of various categories of features in identifying the sentiment for a given twitter tweet. The second system used to preserve the context a Rider Feedback Artificial Tree Optimization-enabled Deep Recurrent neural networks (RFATO-enabled Deep RNN) is developed for the efficient classification of sentiments into various grades. Further to improve the accuracy of classification on n-valued scale Adaptive Rider Feedback Artificial Tree (Adaptive RiFArT)-based Deep Neuro fuzzy network is devised for efficient sentiment grade classification. Finally, this research work proposed a Fuzzy-Rule Based Deep Sentiment Extraction (FBDSE) Algorithm with Deep Sentiment Score computation. Accuracy measure is considered to test the proposed systems performance. It was observed that the fuzzy-rule based system achieved good accuracy compared with machine learning and deep learning based approaches.

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Section: Related Workmentioning

confidence: 99%

“…Finally, the high level vector representation of a tweet𝑆is produced based on the weights by using weighted sum of the word annotations. The S is computed using equation (7).…”

Section: B Attention Layer For Sentiment Orientationmentioning

confidence: 99%

Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

Jasti¹,

Kumar²

2022

IJACSA

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“…Each deep learning method has its positive and negative aspects, therefore, researchers have proposed ensemble techniques with CNN, RNN, and LSTM to avoid the drawbacks of each model [33], [34]. In the same fashion, Yang et al [35] interpolated the output of CNN and LSTM by weighting the predictions of both the models. Generally, RNN has the problem of vanishing gradient descent (i.e., the computation in the long distant layers vanishes after a few layers), so Ding et al [36] proposed to directly connect all layers to reduce the effects of gradient descent.…”

Section: Deep Learning Sentiment Classificationmentioning

confidence: 99%

Context-Aware Sliding Window for Sentiment Classification

Masood

Abbasi

2020

IEEE Access

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Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods. INDEX TERMS Sentiment classification, user history, temporal sliding window, deep learning, machine-learning, temporal dataset.

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“…We begin with basic pre-processing methods (Yang et al, 2017), e.g. splitting a tweet into word, replacing URLs and USERs with normalization patterns <URL> and <USER>, and converting uppercase letters to lowercase letters.…”

Section: Pre-processingmentioning

confidence: 99%

deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

Gao¹,

Chen²

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are five classification models in the proposed multitask learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval-2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval-2018 Task 1 test set.

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deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter

Cited by 6 publications

References 7 publications

Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

Context-Aware Sliding Window for Sentiment Classification

deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

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