Web-Based Traffic Sentiment Analysis: Methods and Applications

2014 ◽  
Vol 15 (2) ◽  
pp. 844-853 ◽  
Author(s):  
Jianping Cao ◽  
Ke Zeng ◽  
Hui Wang ◽  
Jiajun Cheng ◽  
Fengcai Qiao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Web Based ◽  
Analysis Methods

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

On the combination of "off-the-shelf" sentiment analysis methods

2016 ◽  
Author(s):  
Pollyanna Gonçalves ◽  
Daniel Hasan Dalip ◽  
Helen Costa ◽  
Marcos André Gonçalves ◽  
Fabrício Benevenuto
Keyword(s):  
Sentiment Analysis ◽  
Analysis Methods

scholarly journals Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Andrew J Reagan ◽  
Christopher M Danforth ◽  
Brian Tivnan ◽  
Jake Ryland Williams ◽  
Peter Sheridan Dodds
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Analysis Methods

scholarly journals Sentiment Analysis using Logistic Regression and Effective Word Score Heuristic

2018 ◽  
Vol 7 (2.24) ◽  
pp. 20 ◽  
Author(s):  
Abhilasha Tyagi ◽  
Naresh Sharma
Keyword(s):  
Logistic Regression ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Training Sample ◽  
Learning Approaches ◽  
Data Mining Technique ◽  
Validation Data ◽  
Web Based ◽  
Polarity Score ◽  
Speed Up

Sentiment Analysis is a method for judging somebody's sentiment or feeling with respect to a specific thing. It is utilized to recognize and arrange the sentiments communicated in writings. The web-based social networking sites like twitter draws in a huge number of clients that are online for imparting their insights in the form of tweets or comments. The tweets can be then classified into positive, negative, or neutral. In the proposed work, logistic regression classification is used as a classifier and unigram as a feature vector. For accuracy, k fold cross validation data mining technique is used. For choosing precise training sample, tweet subjectivity is utilized. The idea of Effective Word Score heuristic is likewise presented to find the polarity score of words that are frequently used. This additional heuristic can speed up the classification process of sentiments with standard machine learning approaches.  

scholarly journals Forecasting Oil Price Using Web-based Sentiment Analysis

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4291 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Guan-Rong Zeng ◽  
Wen-Jing Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Sentiment Analysis ◽  
Mean Squared Error ◽  
Oil Price ◽  
Future Research ◽  
Price Forecasting ◽  
Web Based ◽  
Market Sentiment ◽  
Root Mean Squared Error ◽  
Strong Intensity ◽  
Squared Error

International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.

scholarly journals Aspect Based Sentiment Analysis using POS Tagging and TFIDF

2019 ◽  
Vol 8 (6) ◽  
pp. 1960-1963 ◽  
Keyword(s):  
Sentiment Analysis ◽  
Support Vector ◽  
The Internet ◽  
Media Content ◽  
Product Reviews ◽  
Interesting Aspect ◽  
Web Based ◽  
Pos Tagging ◽  
Day By Day ◽  
The Web

Social media content on the internet is increasing day by day. Since media knowledge helps people in making decisions, web based businesses give their clients an opportunity to express their opinions about items available on the web in the form of surveys and reviews. Sentiment analysis can be used on product reviews or tweets, comments, blogs to infer individual’s feelings or attitudes. Here Aspect Based Sentiment Analysis is used to extract most interesting aspect of a particular product from unlabeled text. We have developed two models for aspect/feature extraction.Model1 uses POS tagging whereas Model2 utilizes TFIDF .In Model 1 we start with noun phrase algorithm and extend it to adjectives and adverbs to extract all the aspect terms. In model2 after data preprocessing TDIDF technique is used. The relative importances of the aspects are calculated and the most important positive, negative and neutral aspects are presented to the user. Naïve Bayes, Support Vector machine, Decision Tree, KNN were used to classify the sentiment polarity of the generated aspects

scholarly journals User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study (Preprint)

2019 ◽  
Author(s):  
Xinyi Lu ◽  
Long Chen ◽  
Jianbo Yuan ◽  
Joyce Luo ◽  
Jiebo Luo ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Electronic Cigarette ◽  
Web Based ◽  
User Perceptions ◽  
Twitter Data ◽  
Youth And Young Adults ◽  
Data Driven Approach ◽  
Social Media Platforms ◽  
Fruit Flavor

BACKGROUND The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. OBJECTIVE This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. METHODS We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette–related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette–related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. RESULTS More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. CONCLUSIONS The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.

scholarly journals Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guangyao Pang ◽  
Keda Lu ◽  
Xiaoying Zhu ◽  
Jie He ◽  
Zhiyi Mo ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rapid Development ◽  
Auxiliary Information ◽  
Location Model ◽  
Analysis Approach ◽  
Feature Location ◽  
Feature Extraction Method ◽  
Fine Grained ◽  
Analysis Methods ◽  
Benchmark Datasets

With the rapid development of Internet social platforms, buyer shows (such as comment text) have become an important basis for consumers to understand products and purchase decisions. The early sentiment analysis methods were mainly text-level and sentence-level, which believed that a text had only one sentiment. This phenomenon will cover up the details, and it is difficult to reflect people’s fine-grained and comprehensive sentiments fully, leading to people’s wrong decisions. Obviously, aspect-level sentiment analysis can obtain a more comprehensive sentiment classification by mining the sentiment tendencies of different aspects in the comment text. However, the existing aspect-level sentiment analysis methods mainly focus on attention mechanism and recurrent neural network. They lack emotional sensitivity to the position of aspect words and tend to ignore long-term dependencies. In order to solve this problem, on the basis of Bidirectional Encoder Representations from Transformers (BERT), this paper proposes an effective aspect-level sentiment analysis approach (ALM-BERT) by constructing an aspect feature location model. Specifically, we use the pretrained BERT model first to mine more aspect-level auxiliary information from the comment context. Secondly, for the sake of learning the expression features of aspect words and the interactive information of aspect words’ context, we construct an aspect-based sentiment feature extraction method. Finally, we construct evaluation experiments on three benchmark datasets. The experimental results show that the aspect-level sentiment analysis performance of the ALM-BERT approach proposed in this paper is significantly better than other comparison methods.

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