Financial markets sentiment analysis: developing a specialized Lexicon

Yekrangi, Mehdi; Abdolvand, Neda

doi:10.1007/s10844-020-00630-9

Cited by 22 publications

(4 citation statements)

References 35 publications

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“…Sentiment analysis methods based on sentiment lexicon refer to the division of sentiment polarity at different granularities based on the sentiment polarity of sentiment words provided by different sentiment lexicons. Yekrangi [36] and Beigi [37] constructed domain sentiment lexicons based on building upon the basic sentiment lexicon, which could effectively identify and extend on sentiment words in the corpus and improve the classification effect of the domain dataset. Machine-learning-based sentiment analysis methods refer to feature extraction and sentiment classification using machine learning methods with a large amount of annotated corpus information.…”

Section: Methods Of Sentiment Analysismentioning

confidence: 99%

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Meng

2022

Sustainability

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In recent years, the price of small agricultural products has both plummeted and skyrocketed, which has a great impact on people’s lives. Studying the factors affecting the price fluctuation of small agricultural products is of great significance for stabilizing their price. With the development and application of social media, farmers and consumers are more greatly influenced by online public opinion, resulting in irrational planting behavior or purchasing behavior, which has a complex impact on the price of small agricultural products. Taking garlic as an example, we crawled through network public opinions about garlic price from January 2015 to December 2020 using web crawler technology. Then, the network public opinions were quantified using a natural language processing and time-varying parameter vector autoregression (NLP-TVP-VAR) model to empirically analyze their dynamic influence on garlic price fluctuation. It was found that both public attitude and public attention have a short-term influence on garlic price fluctuation, and the influences of each differ according to direction, intensity and timing. The influence of public attitude on garlic price fluctuation is positive, while the influence of public attention on garlic price fluctuation is largely negative. The influence intensity of public attitude is stronger than of public attention on garlic price fluctuation. The influence of public attitude on garlic price fluctuation shows a trend of intensifying, while that of public attention has been weaker than in previous years. In addition, based on the results of our study, we present some recommendations for improving the comprehensive information platform and price fluctuation early warning system for the whole industry chain of small agricultural products.

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Section: Methods Of Sentiment Analysismentioning

confidence: 99%

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Meng

2022

Sustainability

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“…In contrast, the latter was to exploit the syntactic pattern of co-occurrence words in the corpus to compile the sentiment wordlist. Other than this, the study [51] attempted to combine both approaches to develop a specialized financial lexicon statistically and semantically.…”

Section: Sentiment-based Stock Forecasting Approachmentioning

confidence: 99%

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

et al. 2022

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Stock forecasting is a significant and challenging task. The recent development of web technologies has transformed the communication channel to allow the public to share information over the web such as news, social media contents, etc., thus causing exponential growth of web data. The massively available information might be the key to revealing the financial market’s unexplained variability and facilitating forecasting accuracy. However, this information is usually in unstructured natural language and consists of different inherent meanings. Although a human can easily interpret the inherent messages, it is still complicated to manually process such a massive amount of textual data due to the constraint of time, ability, energy, etc. Due to the different properties of text sources, it is crucial to understand various text processing approaches to optimize forecasting performance. This study attempted to summarize and discuss the current text-based financial forecasting approaches in the aspect of semantic-based, sentiment-based, event-extraction-based, and hybrid approaches. Afterward, the study discussed the strength and weakness of each approach, followed with their comparison and suitable application scenarios. Moreover, this study also highlighted the future research direction in text-based stock forecasting, where the overall discussion is expected to provide insightful analysis for future reference.

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“…Unlike the general lexicon mentioned above, there are specialized lexicons for specific domains. Yekrangi & Abdolvand, ( 2021 ) constructed a lexicon for the financial market’s domain. The authors evaluated its performance by calculating the correlation between dollar price trends and sentiment scores, which averages 60% due to the finance-specific words in the specialized lexicon.…”

Section: Related Workmentioning

confidence: 99%

SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

Kanfoud

Bouramoul

2022

J Intell Inf Syst

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The main objective of multilingual sentiment analysis is to analyze reviews regardless of the original language in which they are written. Switching from one language to another is very common on social media platforms. Analyzing these multilingual reviews is a challenge since each language is different in terms of syntax, grammar, etc. This paper presents a new language-independent representation approach for sentiment analysis, SentiCode. Unlike previous work in multilingual sentiment analysis, the proposed approach does not rely on machine translation to bridge the gap between different languages. Instead, it exploits common features of languages, such as part-of-speech tags used in Universal Dependencies. Equally important, SentiCode enables sentiment analysis in multi-language and multi-domain environments simultaneously. Several experiments were conducted using machine/deep learning techniques to evaluate the performance of SentiCode in multilingual (English, French, German, Arabic, and Russian) and multi-domain environments. In addition, the vocabulary proposed by SentiCode and the effect of each token were evaluated by the ablation method. The results highlight the 70% accuracy of SentiCode, with the best trade-off between efficiency and computing time (training and testing) in a total of about 0.67 seconds, which is very convenient for real-time applications.

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Financial markets sentiment analysis: developing a specialized Lexicon

Cited by 22 publications

References 35 publications

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

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