scholarly journals Star Business Value Prediction based on Sentiment Analysis

2021 ◽  
Vol 13 ◽  
pp. 229-232
Author(s):  
Meixuan Li
Keyword(s):  
Sentiment Analysis ◽  
Business Value ◽  
Vector Model ◽  
Value Prediction ◽  
Talk Show

The article crawls the audience comments from some videos on the YouTube platform of "Talk Show Conference Season 3", extracts relevant content about popular champions Wang Mian and Wang Jianguo for sentiment analysis, uses Google Data API to design a crawler to obtain comment content, and to crawl After the received content is preprocessed, Word2vec is used to build a word vector model, and finally an LSTM model is built for training prediction. It can be seen that the popularity of the player Wang Mian is higher than that of Wang Jianguo. The entertainment company where the two are located can adjust the artist's work according to the changes in the public's love for the players.

Sentiment Analysis Technologies in AliMe — An Intelligent Assistant for E-commerce

2021 ◽  
pp. 2050016
Author(s):  
Shuangyong Song ◽  
Chao Wang ◽  
Siyang Liu ◽  
Haiqing Chen ◽  
Huan Chen ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Customer Service ◽  
User Satisfaction ◽  
Business Value ◽  
Analysis Framework ◽  
Artificial Intelligent ◽  
Single Task ◽  
Sentiment Detection ◽  
Intelligent Assistant ◽  
Key Techniques

In this paper, we introduce a sentiment analysis framework and its corresponding key techniques used in AliMe, an artificial intelligent (AI) assistant for e-commerce customer service, whose fundamental ability of sentiment analysis provides support for five upper-layer application modules: user sentiment detection, user sentiment comfort, sentimental generative chatting, user service quality control and user satisfaction prediction. Detailed implementation of each module is demonstrated and experiments show our framework not only performs well on each single task but also manifests its competitive business value as a whole.

Sentiment Information to Vector, a more Automatic Approach for Sentiment Analysis

2014 ◽  
Vol 945-949 ◽  
pp. 3418-3423
Author(s):  
Lin Du ◽  
Wei Ran Xu ◽  
Ping Yang Liu
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Syntactic Structure ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Polarity Reversal ◽  
Vector Model ◽  
Sentence Level

As sentence level sentiment analysis having been studied extensively, it has been proven that the syntactic structure of a sentence usually holds important information for sentiment analysis, especially for handling polarity reversal. However, the previous attempts of adopting such structural information mainly focus on making certain predefined rules which requires large linguistic expertise of the rule-maker,and the procedure itself is often manually labored and time consuming. To solve this problem, in this paper we propose a novel simple vector model to represent a sentence’s syntactic structure and its prior sentiment information uniformly and rapidly. Experiment results show that our proposed approach performs well in COAE 2013 dataset, and could also be used for machine learning algorithms to extract more distinguish features automatically.

scholarly journals CarEnvision: A Data-Driven Machine Learning Framework for Automated Car Value Prediction

2021 ◽  
Author(s):  
TianGe (Terence) Chen ◽  
Angel Chang ◽  
Evan Gunnell ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Retail Price ◽  
Data Driven ◽  
Value Prediction ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Negative Sentiment

When people want to buy or sell a personal car, they struggle to know when the timing is best in order to buy their favorite vehicle for the best price or sell for the most profit. We have come up with a program that can predict each car’s future values based on experts’ opinions and reviews. Our program extracts reviews which undergo sentiment analysis to become our data in the form of positive and negative sentiment. The data is then collected and used to train the Machine Learning model, which will in turn predict the car’s retail price.

scholarly journals Sentiment Analysis About Indonesian Lawyers Club Television Program Using K-Nearest Neighbor, Naïve Bayes Classifier, And Decision Tree

2021 ◽  
Vol 8 (1) ◽  
pp. 50-56
Author(s):  
Nico Nathanael Wilim ◽  
Raymond Sunardi Oetama
Keyword(s):  
Decision Tree ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Naïve Bayes Classifier ◽  
Talk Show

Indonesia Lawyers Club (ILC) is a talk show on TVOne that discusses topics around public phenomena, legal issues, crime, and other similar topics. In 2018, ILC won the Panasonic Gobel Awards as the best news talk show program. But in 2019, ILC failed to win the award which was won by Mata Najwa which featured a talk show event that appeared on Trans7. As one of the television shows that has won awards, ILC has pros and cons for its shows from the public. This study applies a sentiment analysis approach to examine public opinion on Twitter about Mata Najwa and ILC in 2018 and 2019. This study applies K-Nearest Neighbor, Naïve Bayes Classifier, and Decision Tree classification algorithm to validate the result. The contribution of this study is to show that public opinion on Twitter can be examined to figure out community sentiment on a tv talk show as well as to confirm the Award winner of tv Talkshow.   Index Terms—datamining; Decision Tree; K-NN; Naïve Bayes Classifier; sentiment analysis

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

Building Business Value: The New Path to Preferred Supplier Status

The Synergist ◽  
2005 ◽  
Vol 16 (2) ◽  
pp. 28
Author(s):  
Joe Bialowitz
Keyword(s):  
Business Value

The Potential Of Blockchain Technology To Change International Mobile Roaming Business Model

2019 ◽  
Vol 5 (1) ◽  
pp. 15-22
Author(s):  
Ardian Thresnantia Atmaja
Keyword(s):  
Business Model ◽  
Current Situation ◽  
Business Value ◽  
Third Party ◽  
Fundamental Aspect ◽  
Clearing House ◽  
Blockchain Technology ◽  
Smart Contract ◽  
Target Architecture ◽  
Architecture Development

The key objectives of this paper is to propose a design implementation of blockchain based on smart contract which have potential to change international mobile roaming business model by eliminating third-party data clearing house (DCH). The analysis method used comparative analysis between current situation and target architecture of international mobile roaming business that commonly used by TOGAF Architecture Development Method. The purposed design of implementation has validated the business value by using Total Cost of Ownership (TCO) calculation. This paper applies the TOGAF approach in order to address architecture gap to evaluate by the enhancement capability that required from these three fundamental aspect which are Business, Technology and Information. With the blockchain smart contract solution able to eliminate the intermediaries Data Clearing House system, which impacted to the business model of international mobile roaming with no more intermediaries fee for call data record (CDR) processing and open up for online billing and settlement among parties. In conclusion the business value of blockchain implementation in the international mobile roaming has been measured using TCO comparison between current situation and target architecture that impacted cost reduction of operational platform is 19%. With this information and understanding the blockchain technology has significant benefit in the international mobile roaming business.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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