Applying sentiment analysis in social web for smart decision support marketing

Wu, Shih-Jung; Chiang, Rui‐Dong; Chang, Han-Chi

doi:10.1007/s12652-018-0683-9

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…These recommendation systems assist promotion-focused customers in discovering suitable products and facilitate their impulsive buying behavior. Regarding prevention-focused customers, electronic word-of-mouth (EWOM) can be a reliable source as it can reduce anxiety while making a purchase decision [24]. Website security and reliable transactions also help reduce prevention-focused customers' anxiety and enhance purchases [25].…”

Section: Introductionmentioning

confidence: 99%

How Personality Traits Affect Customer Empathy Expression of Social Media Ads and Purchasing Intention: A Psychological Perspective

Demirci,

Ling,

Lee

et al. 2024

JTAER

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Consumers’ personality traits significantly influence their perceptions regarding social media advertising. While prior research on consumers’ purchasing intentions in social networking sites advertising has mainly focused on advertising valence antecedents, it is crucial to recognize that consumers’ susceptibility to advertising persuasion, particularly in terms of empathic expression, varies based on a key criterion: whether consumers are driven to attain a specific desired state or are more inclined to avoid an undesirable state. Regulatory Focus Theory (RFT) posits that individuals operate under distinct motivational mechanisms that govern their determination to achieve desired goals, influencing how they process and evaluate advertising messages. In light of RFT, we conducted an online survey with 524 valid responses, utilizing partial least squares (PLS) for research model analysis. The findings revealed that promotion-focused individuals have positively influenced perceptions of social media ad effectiveness (informativeness, ad creativity, perceived relevance, and emotional appeal). In contrast, prevention-focused individuals negatively perceived social media ad effectiveness. Furthermore, this study highlighted that perceived relevance and emotional appeal have a more significant impact on attitudes toward expressing empathy than informativeness and ad creativity.

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Section: Introductionmentioning

confidence: 99%

How Personality Traits Affect Customer Empathy Expression of Social Media Ads and Purchasing Intention: A Psychological Perspective

Demirci,

Ling,

Lee

et al. 2024

JTAER

View full text Add to dashboard Cite

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“…On the Internet, consumers enthusiastically share their opinions and reviews via news, blogs, and social media, also known as electronic word of mouth (eWOM), and increasing numbers of potential buyers habitually consult eWOM before making their purchasing decisions [9][10][11][12][13][14]. Since eWOM can be positive or negative statements about a product or company [15][16][17], researchers have proposed sentiment analysis methods for automatically distinguishing three types of eWOM: positive, negative, and neutral [18]. To simultaneously apply historical sales data and eWOM to car sales forecasting, Fan et al [2] used a sentiment analysis method, the Naive Bayes (NB) algorithm, to extract the sentiment index from each online review, and then integrated the sentiment index into the imitation coefficient of the Bass/Norton model to improve the forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improving the Forecasting Performance of Taiwan Car Sales Movement Direction Using Online Sentiment Data and CNN-LSTM Model

2022

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The automotive industry is the leading producer of machines in Taiwan and worldwide. Developing effective methods for forecasting car sales can allow car companies to arrange their production and sales plans. Capitalizing on the growth of social media and deep learning algorithms, this research aimed to improve the overall performance of the forecasting of Taiwan car sales movement direction forecasting by using online sentiment data and CNN-LSTM method. First, the historical sales volumes and multi-channel online sentiment data for six car brands in Taiwan were collected and preprocessed for labeling of car sales movement direction. Then, three models, namely, the classical, sentimental, and CNN-LSTM models, were constructed and trained/fitted for forecasting car sales movement directions in Taiwan. Finally, the performance of the three prediction models were compared to verify the effects of online sentiment data and the CNN-LSTM model on forecasting performance. The results showed that four forecasting performance indices, i.e., accuracy, precision, recall and F1-score, improved by 27.78% (from 41.67% to 69.45%), 0.39 (from 0.38 to 0.77), 0.27 (from 0.42 to 0.69) and 0.33 (from 0.35 to 0.68), respectively. Therefore, the online sentiment data and CNN-LSTM method can indeed improve the overall performance of car sales movement direction in Taiwan.

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