Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

Uhr, Patrick; Zenkert, Johannes; Fathi, Madjid

doi:10.1109/smc.2014.6974028

Cited by 9 publications

(5 citation statements)

References 16 publications

(15 reference statements)

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“…Again, a high correlation between the stock prices and the extracted sentiments was found. Later, in 2014 Uhr et al [36] introduced a method for sentiment analysis in financial markets combining word associations through the 'Concept for the Imitation of the Mental Ability of Word Association' (CIMAWA) and lexical resources. They evaluated the evolution of stock prices as compared to the sentiment measures calculated from a news corpus with 918,427 finance-related German documents.…”

Section: B Sentiment Analysis In the Financial Domainmentioning

confidence: 99%

Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

2023

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Texts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context. This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular. For lowresource languages such as Spanish, this situation becomes even more acute. Yet, the latest advancements in the field, including word embeddings and transformers, have allowed to boost the performance of Sentiment Analysis solutions. In this work we explore the impact of the combination of different feature sets in the accuracy of Sentiment Analysis in Spanish financial texts. For this, a corpus with 15,915 tweets has been compiled and manually annotated as either positive, negative, or neutral. Then, feature sets based on contextual and non-contextual embeddings along with linguistic features were evaluated both individually and combined. The best results, with a weighted F1-score of 73.15880%, were obtained with a combination of feature sets by means of knowledge integration.

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Section: B Sentiment Analysis In the Financial Domainmentioning

confidence: 99%

Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

2023

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“…A model is suggested to forecast the stock prices based on the combining of a Self-Organizing Map (SOM) and fuzzy -Support Vector Machines (f-SVM) (Naseem et al, 2018). Uhr et al (2014) applies the sentiment analysis on financial market news implementing a software prototype for computing results on different levels of exploration, splitting text into sentences and subsets of tokens from sentences. SentimentWortschatz (SentiWS) software (Remus et al, 2010) was used.…”

Section: Sentiment Analysismentioning

confidence: 99%

Analysing the sentiments about the education system trough Twitter

2023

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This paper applies Information and Communication Technologies (ICT) as well as data analysis to gain a better understanding of the existing perception on the education system. 45,278 tweets were downloaded and processed. Using a lexicon-based approach, examining the most frequently used words, and estimating similarities between terms, we detected that a predominantly negative perception of the education system exists in most of the analysed countries. A positive perception is identified in certain low-income nations. Men exhibit a more positive sentiment than women as well as a higher subjectivity in some countries. The countries that exhibit the most positive perceptions India, Canada, Pakistan, Australia, South Africa and Kenya are also those that manifest the highest subjectivity.

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“…New approaches in sentiment analysis are emotion detection, transfer learning, building resources [46] or the analysis of word associations for sentiment evaluation [54]. Emotion detection is a task of sentiment analysis, which in contrast to classical polarity detection is divided not only in positive and negative.…”

Section: Sentiment Analysismentioning

confidence: 99%

Knowledge discovery in multidimensional knowledge representation framework

2018

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Visualization of results is one of the central challenges in big data analytics and integrative text mining. With a growing amount of unstructured data and different perspectives on big data, knowledge graphs have difficulties to simultaneously represent and visualize all analyzed dimensions of knowledge. This paper proposes integrative text mining as a solution to combine results from different dimensional analysis in a multidimensional knowledge representation (MKR) for knowledge discovery and visualization purpose. Analysis results from named entity recognition, topic detection, sentiment analysis and the extraction of semantic relationships are, therefore, integrated into a common representation structure. In the implementation part of this research, an application is introduced which utilizes MKR based on the results of stated text mining methods applied on a German and English news data set. State-of-the-art visualizations are used in the application and MKR adaptively transforms the visualization type of the knowledge graph according to the selected context for knowledge discovery.

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Sentiment analysis in financial markets A framework to utilize the human ability of word association for analyzing stock market news reports

Cited by 9 publications

References 16 publications

Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

Analysing the sentiments about the education system trough Twitter

Knowledge discovery in multidimensional knowledge representation framework

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