Sentiment Analysis Using Automatically Labelled Financial News Items

Généreux, Michel; Poibeau, Thierry; Koppel, Moshe

doi:10.1007/978-94-007-1757-2_9

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…An n-gram model is a sequence of 𝑛 consecutive symbols that can be characters, words, bytes, or any other continuous symbols. For instance, a 1-gram (or unigram) is one-symbol, a 2-symbol (bigram) is a two-symbol sequence of symbols, and 3-gram (trigram) is a three-symbol sequence of symbols so on and so forth (Généreux et al, 2011;Zhai et al, 2011). It uses the previous 𝑛 − 1 symbol to predict the next 𝑝(𝑡 𝑛 |𝑡 𝑛−1 ).…”

Section: N-gram Modelmentioning

confidence: 99%

“…Most of the current studies (Koppel and Shtrimberg, 2006;Groth and Muntermann, 2011;Yu et al, 2013) on sentiment-based financial news analysis typically rely on simple frequency-based textual presentations, such as Bag-of-Words (BoW) in which each piece of news is represented by the occurrence frequencies of distinct words. Some other research works (Généreux et al, 2011;Zhai et al, 2011), in contrast, have employed unigram text characterization, which shows similar characteristics to the BoW approach thanks to their commonality in consideration of occurrence frequency. However, in coping with a large volume of data, the process is encountered with a lot of the low frequency bigrams which can be considered as an informative and sentiment feature while the extraction of words is based on their high frequency which leads to ignore low frequency-based linguist features that can be worth to sentiment classification [9].…”

Section: Introductionmentioning

confidence: 99%

“…This research focuses closely on related studies that explore the impact of different feature types as input for SVM classification. this research is partly related to Généreux et al (2011). The authors employed the different types of features as unigrams, stems, financial terms, health metaphors and agent-metaphors along with some feature selection methods (Information Gain (IG), (Chi-Square (CHI) and Document Frequency (DF)) and two feature weighting methods (binary and TF (Term Frequency) which assign different values to each feature.…”

Section: Introductionmentioning

confidence: 99%

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NgramPOS: a bigram-based linguistic and statistical feature process model for unstructured text classification

2018

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Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. With the ever growing social networking and online marketing sites, the reviews obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like clustering or classification to provide different polarity categories such as positive and negative in order to extract a meaningful information for future uses. Accordingly, in this study we investigate the use of Natural Language processing (NLP) in a way to improve the sentiment classification performance to evaluate the information content of financial news as an instrument for using in investment decisions system. Since the proposed feature extraction approach is based on the occurrence frequency of words, low-frequency linguist features that could be critical in sentiment classification are typically ignored. In this research, therefore, we attempt to improve current sentiment analysis approaches for financial news classification in consideration of low-frequency, informative, linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for text sentiment analysis and classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy than other types feature sets.

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Section: N-gram Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NgramPOS: a bigram-based linguistic and statistical feature process model for unstructured text classification

2018

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“…In a similar vein, Généreux et al (2011) investigate the impact of financial news items on the stock price of companies. They treat short financial news snippets about companies as if they were carrying implicit sentiment about the future market direction made explicit by the vocabulary they employ.…”

Section: Related Workmentioning

confidence: 99%

Do Enterprises Have Emotions?

Buechel

Hahn

Goldenstein

et al. 2016

Proceedings of the 7th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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Emotional language of human individuals has been studied for quite a while dealing with opinions and value judgments people have and share with others. In our work, we take a different stance and investigate whether large organizations, such as major industrial players, have and communicate emotions, as well. Such an anthropomorphic perspective has recently been advocated in management and organization studies which consider organizations as social actors. We studied this assumption by analyzing 1,676 annual business and sustainability reports from 90 top-performing enterprises in the United States, Great Britain and Germany. We compared the measurements of emotions in this homogeneous corporate text corpus with those from RCV1, a heterogeneous Reuters newswire corpus. From this, we gathered empirical evidence that business reports compare well with typical emotion-neutral economic news, whereas sustainability reports are much more emotionally loaded, similar to emotion-heavy sports and fashion news from Reuters. Furthermore, our data suggest that these emotions are distinctive and relatively stable over time per organization, thus constituting an emotional profile for enterprises.

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Positive or Negative? A semantic orientation of financial news

Kanmaz

Sürer

2019

2019 27th Signal Processing and Communications Applications Conference (SIU)

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Sentiment Analysis Using Automatically Labelled Financial News Items

Cited by 6 publications

References 4 publications

NgramPOS: a bigram-based linguistic and statistical feature process model for unstructured text classification

NgramPOS: a bigram-based linguistic and statistical feature process model for unstructured text classification

Do Enterprises Have Emotions?

Positive or Negative? A semantic orientation of financial news

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