scholarly journals Deep Learning for Social Media Sentiment Analysis

MATEMATIKA ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 99-111
Author(s):  
Kartika Fithriasari ◽  
Saidah Zahrotul Jannah ◽  
Zakya Reyhana
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Characteristic Curve ◽  
Sentiment Classification ◽  
Radio Station ◽  
Learning Methods ◽  
Text Data ◽  
Media Sentiment

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

scholarly journals An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism

2019 ◽  
Vol 11 (4) ◽  
pp. 96 ◽  
Author(s):  
Li ◽  
Liu ◽  
Zhang ◽  
Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Linguistic Knowledge ◽  
Learning Methods ◽  
Classification Tasks ◽  
Text Sentiment Analysis

Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.

scholarly journals A Framework for Sentiment Analysis of Telugu Tweets

2020 ◽  
Vol 9 (6) ◽  
pp. 523-525
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
English Language ◽  
Research Work ◽  
Learning Approaches ◽  
Text Data

Now a day Social Media like Facebook, twitter and Instagram is major Sources for people to share their emotions based on the current situations in society. By knowing the interesting patterns in it, a government/appropriate person for that situation can take good and useful decisions. Sentiment analysis is a method where people can extract the useful information from the text like the emotions (happy, sad, and neutral) of people. Much research work was been underdoing in the area of sentiment analysis. Among that work the Machine learning and Deep learning approaches plays a maximum role. Existing works on sentiment analysis is going in the English language. In this paper, proposed a novel framework that specifically designed to do sentiment analysis of the text data, that available in the telugu language. The proposed framework was integrated with the word embedding model Word2Vec, language translator and deep learning approaches like Recurrent Neural Network and Navie base algorithms to collect and analyse the sentiment in tweeter data that present in telugu language. The results shows effective in terms of accuracy, precision and specificity.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter

2020 ◽  
Vol 10 (22) ◽  
pp. 8035
Author(s):  
Jenq-Haur Wang ◽  
Ting-Wei Liu ◽  
Xiong Luo
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Short Term Memory ◽  
Absolute Error ◽  
User Participation ◽  
Sentiment Classification ◽  
Context Dependency ◽  
Long Distance ◽  
Learning Methods ◽  
The Public

With the wide popularity of social media, it’s becoming more convenient for people to express their opinions online. To better understand what the public think about a topic, sentiment classification techniques have been widely used to estimate the overall orientation of opinions in post contents. However, users might have various degrees of influence depending on their participation in discussions on different topics. In this paper, we address the issues of combining sentiment classification and link analysis techniques for extracting stances of the public from social media. Since social media posts are usually very short, word embedding models are first used to learn different word usages in various contexts. Then, deep learning methods such as Long Short-Term Memory (LSTM) are used to learn the long-distance context dependency among words for better estimation of sentiments. Third, we consider the major user participation in popular social media by adjusting the users weights to reflect their relative influence in user-post interaction graphs. Finally, we combine post sentiments and user influences into a total opinion score for extracting public stances. In the experiments, we evaluated the performance of our proposed approach for tweets about the 2016 U.S. Presidential Election. The best performance of sentiment classification can be observed with an F-measure of 72.97% for LSTM classifiers. This shows the effectiveness of deep learning methods in learning word usage in social media contexts. The experimental results on stance extraction showed the best performance of 0.68% Mean Absolute Error (MAE) in aggregating public stances on election candidates. This shows the potential of combining tweet sentiments and user participation structures for extracting the aggregate stances of the public on popular topics. Further investigation is needed to verify the performance in different social media sources.

scholarly journals Sentiment Classification for Financial Texts Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shanshan Dong ◽  
Chang Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Loss Function ◽  
Classification Accuracy ◽  
Sentiment Classification ◽  
Target Domain ◽  
Text Data ◽  
Source Domain ◽  
Cross Domain

Sentiment classification for financial texts is of great importance for predicting stock markets and financial crises. At present, with the popularity of applications in the field of natural language processing (NLP) adopting deep learning, the application of automatic text classification and text-based sentiment classification has become more and more extensive. However, in the field of financial text-based sentiment classification, due to a lack of labeled samples, such applications are limited. A domain-adaptation-based financial text sentiment classification method is proposed in this paper, which can adopt source domain (SD) text data with sentiment labels and a large amount of unlabeled target domain (TD) financial text data as training samples for the proposed neural network. The proposed method is a cross-domain transfer-learning-based method. The domain classification subnetwork is added to the original neural network, and the domain classification loss function is also added to the original training loss function. Therefore, the network can simultaneously adapt to the target domain and then accomplish the classification task. The experiment of the proposed sentiment classification transfer learning method is carried out through an open-source dataset. The proposed method in this paper uses the reviews of Amazon Books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates can reach 65.0%, 61.2%, 61.6%, and 66.3%, respectively. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively.

scholarly journals Detection of Sentiment Analysis in Social Media using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 636-643
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Activation Function ◽  
Sentiment Classification ◽  
Network Layers ◽  
Sentiment Dictionary ◽  
Lstm Network ◽  
The Military

With the rapid climb of web page from social media, such studies as online opinion mining or sentiment analysis of text have started receiving attention from government, industry, and academic sectors. In recent years, sentiment analysis has not only emerged under knowledge fusion within the big data era, but has also become a well-liked research topic within the area of AI and machine learning. This study used the Military life PTT board of Taiwan’s largest online forum because the source of its experimental data. the aim of this study was to construct a sentiment analysis framework and processes for social media so as to propose a self-developed military sentiment dictionary for improving sentiment classification and analyze the performance of various deep learning models with various parameter calibration combinations. The experimental results show that the accuracy and F1-measure of the model that mixes existing sentiment dictionaries and therefore the self-developed military sentiment dictionary are better than the results from using existing sentiment dictionaries only. Furthermore, the prediction model trained using the activation function, Tanh, and when the amount of Bi-LSTM network layers is 2, the accuracy and F1-measure have a good better performance for sentiment classification.

scholarly journals Tourism destination management using sentiment analysis and geo-location information: a deep learning approach

2021 ◽  
Author(s):  
Marina Paolanti ◽  
Adriano Mancini ◽  
Emanuele Frontoni ◽  
Andrea Felicetti ◽  
Luca Marinelli ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Geographical Location ◽  
Word Level ◽  
Tourism Sector ◽  
Tourist Flows ◽  
Media Sentiment

AbstractSentiment analysis on social media such as Twitter is a challenging task given the data characteristics such as the length, spelling errors, abbreviations, and special characters. Social media sentiment analysis is also a fundamental issue with many applications. With particular regard of the tourism sector, where the characterization of fluxes is a vital issue, the sources of geotagged information have already proven to be promising for tourism-related geographic research. The paper introduces an approach to estimate the sentiment related to Cilento’s, a well known tourism venue in Southern Italy. A newly collected dataset of tweets related to tourism is at the base of our method. We aim at demonstrating and testing a deep learning social geodata framework to characterize spatial, temporal and demographic tourist flows across the vast of territory this rural touristic region and along its coasts. We have applied four specially trained Deep Neural Networks to identify and assess the sentiment, two word-level and two character-based, respectively. In contrast to many existing datasets, the actual sentiment carried by texts or hashtags is not automatically assessed in our approach. We manually annotated the whole set to get to a higher dataset quality in terms of accuracy, proving the effectiveness of our method. Moreover, the geographical coding labelling each information, allow for fitting the inferred sentiments with their geographical location, obtaining an even more nuanced content analysis of the semantic meaning.

Sign in / Sign up

Export Citation Format

Share Document