As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain ...As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.展开更多
Nowadays,the impact of emerging social media on the accounting is still a relatively new field and none of the existing research has explored the correlation among the public attitude towards social media,official acc...Nowadays,the impact of emerging social media on the accounting is still a relatively new field and none of the existing research has explored the correlation among the public attitude towards social media,official accounting attitude and the performance of the stock prices of listed firms.U sing the state-of-the-art sentiment analysis tool and 25 public companies'dataset from Yahoo Finance,the correlations among the company's stock price,sentiment in twitter and sentiment in earnings report are quantitatively studied in this paper.Hypothesis testing is used to infer the result of two proposed hypotheses on the sample data.The results demonstrate that(1)there is a significant negative correlation between company's stock price and sentiment in its corresponding earnings reports,and(2)there is no statistical significance for the correlation between company's stock price and sentiment in its corresponding Twitter data.展开更多
Bike sharing is considered a state-of-the-art transportation program. It is ideal for short or medium trips providing riders the ability to pick up a bike at any self-serve bike station and return it to any bike stati...Bike sharing is considered a state-of-the-art transportation program. It is ideal for short or medium trips providing riders the ability to pick up a bike at any self-serve bike station and return it to any bike station located within the system’s coverage area. The bike sharing programs in the United States are still very young compared to those in European countries. Washington DC was the first jurisdiction to devise a third generation bike sharing system in the US in 2008. To evaluate the popularity of a bike sharing program, a sentiment analysis of the riders’ feedback can be performed. Twitter is a great platform to understand people’s views instantly. Social media mining is, thus, gaining popularity in many research areas including transportation. Social media mining has two major advantages over conventional attitudinal survey methods—it can easily reach a large audience and it can reflect the true behavior of participants because of the anonymity social media provides. It is known that self-imposed censor is common in responding to conversational attitudinal surveys. This study performed text mining on the tweets related to a case study (Capital Bike share of Washington DC) to perform sentiment analysis or opinion mining. The results of the text mining mostly revealed higher positive sentiments towards the current system.展开更多
Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in pre...Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in prediction of financial markets. In this paper, seven different techniques of data mining are applied to predict stock price movement of Shanghai Composite Index. The approaches include Support vector machine, Logistic regression, Naive Bayesian, K-nearest neighbor classification, Decision tree, Random forest and Adaboost. Extracting the corresponding comments between April 2017 and May 2018, it shows that: 1) sentiment derived from Eastmoney, a social media platform for the financial community in China, further enhances model performances, 2) for positive and negative sentiments classifications, all classifiers reach at least 75% accuracy and the linear SVC models prove to perform best, 3) according to the strong correlation between the price fluctuation and the bullish index, the approximate overall trend of the closing price can be acquired.展开更多
文摘As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.
文摘Nowadays,the impact of emerging social media on the accounting is still a relatively new field and none of the existing research has explored the correlation among the public attitude towards social media,official accounting attitude and the performance of the stock prices of listed firms.U sing the state-of-the-art sentiment analysis tool and 25 public companies'dataset from Yahoo Finance,the correlations among the company's stock price,sentiment in twitter and sentiment in earnings report are quantitatively studied in this paper.Hypothesis testing is used to infer the result of two proposed hypotheses on the sample data.The results demonstrate that(1)there is a significant negative correlation between company's stock price and sentiment in its corresponding earnings reports,and(2)there is no statistical significance for the correlation between company's stock price and sentiment in its corresponding Twitter data.
文摘Bike sharing is considered a state-of-the-art transportation program. It is ideal for short or medium trips providing riders the ability to pick up a bike at any self-serve bike station and return it to any bike station located within the system’s coverage area. The bike sharing programs in the United States are still very young compared to those in European countries. Washington DC was the first jurisdiction to devise a third generation bike sharing system in the US in 2008. To evaluate the popularity of a bike sharing program, a sentiment analysis of the riders’ feedback can be performed. Twitter is a great platform to understand people’s views instantly. Social media mining is, thus, gaining popularity in many research areas including transportation. Social media mining has two major advantages over conventional attitudinal survey methods—it can easily reach a large audience and it can reflect the true behavior of participants because of the anonymity social media provides. It is known that self-imposed censor is common in responding to conversational attitudinal surveys. This study performed text mining on the tweets related to a case study (Capital Bike share of Washington DC) to perform sentiment analysis or opinion mining. The results of the text mining mostly revealed higher positive sentiments towards the current system.
文摘Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in prediction of financial markets. In this paper, seven different techniques of data mining are applied to predict stock price movement of Shanghai Composite Index. The approaches include Support vector machine, Logistic regression, Naive Bayesian, K-nearest neighbor classification, Decision tree, Random forest and Adaboost. Extracting the corresponding comments between April 2017 and May 2018, it shows that: 1) sentiment derived from Eastmoney, a social media platform for the financial community in China, further enhances model performances, 2) for positive and negative sentiments classifications, all classifiers reach at least 75% accuracy and the linear SVC models prove to perform best, 3) according to the strong correlation between the price fluctuation and the bullish index, the approximate overall trend of the closing price can be acquired.
