TY - GEN
T1 - Analysing Tweets Sentiments for Investment Decisions in the Stock Market
AU - Hao, Zhicheng
AU - Chen-Burger, Yun-Heh Jessica
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021/6/8
Y1 - 2021/6/8
N2 - The increasing practice of using social media as the basis for decision-making has made social media an important alternative information source. This is, in particular, true for investors in the stock market due to their needs to gain dynamic, real-time information and strategic persons’ views. It is therefore very interesting to investigate the relationships between the sentiments of the text as published on social media and how they may influence investors’ minds. In this paper, we selected several influential Twitter accounts, inc. Bloomberg, Forbes, Reuters, WSJ and Donald Trump, for sentiment analysis using SentiStrength. We found a fair amount of agreement between the sentiments as generated by the tool and those assigned from investors’ point of view, esp. when plenty of positive words have been used in Tweets. However, we also discovered that not all Tweets with many positive words may generate positive sentiments in investors’ minds. Furthermore, we identified interesting differentiated sentiments expressed in different Tweeter accounts that may indicate the stance of their holders, e.g. using an upbeat tone thus to promote economic growth; or being conservative, thus maintaining one’s authority. Overall, we found many Tweets scored a neutral sentiment, as many of them contain references that their views cannot be determined without examining additional sources.
AB - The increasing practice of using social media as the basis for decision-making has made social media an important alternative information source. This is, in particular, true for investors in the stock market due to their needs to gain dynamic, real-time information and strategic persons’ views. It is therefore very interesting to investigate the relationships between the sentiments of the text as published on social media and how they may influence investors’ minds. In this paper, we selected several influential Twitter accounts, inc. Bloomberg, Forbes, Reuters, WSJ and Donald Trump, for sentiment analysis using SentiStrength. We found a fair amount of agreement between the sentiments as generated by the tool and those assigned from investors’ point of view, esp. when plenty of positive words have been used in Tweets. However, we also discovered that not all Tweets with many positive words may generate positive sentiments in investors’ minds. Furthermore, we identified interesting differentiated sentiments expressed in different Tweeter accounts that may indicate the stance of their holders, e.g. using an upbeat tone thus to promote economic growth; or being conservative, thus maintaining one’s authority. Overall, we found many Tweets scored a neutral sentiment, as many of them contain references that their views cannot be determined without examining additional sources.
KW - Sentiment analysis
KW - SentiStrength
KW - Stock market price
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85111171766&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-2994-5_11
DO - 10.1007/978-981-16-2994-5_11
M3 - Conference contribution
AN - SCOPUS:85111171766
SN - 9789811629938
T3 - Smart Innovation, Systems and Technologies
SP - 129
EP - 141
BT - Agents and Multi-Agent Systems
PB - Springer
T2 - 15th International KES Conference on Agent and Multi-Agent Systems-Technologies and Applications 2021
Y2 - 14 June 2021 through 16 June 2021
ER -