Lexicon-based Sentiment Analysis for Economic and Financial Applications

This package allows Python users to leverage on cutting-hedge NLP techniques to easily run sentiment analysis on economic text. Given a list of texts as input and a list of tokens of interest (ToI), the algorithm analyses the texts and compute the economic sentiment associated each ToI. Two key features characterize this approach. First, it is fine-grained, since words are assigned a polarity score that ranges in [-1,1] based on a dictionary. Second, it is aspect-based, since the algorithm selects the chunk of text that relates to the ToI based on a set of semantic rules and calculates the sentiment only on that text, rather than the full article.

The package includes some additional of features, like automatic negation handling, tense detection, location filtering and excluding some words from the sentiment computation. The algorithm only supports English language, as it relies on the en_core_web_lg language model from the spaCy Python module.

Installation

Make sure to install the following libraries:

pip install utm lxml numpy fastavro h5py spacy nltk matplotlib joblib toolz textblob gensim pandas

pip install git+https://github.com/hanzhichao2000/pysentiment
pip install sentence_splitter wordcloud ruamel-yaml  pycosat

Set up NLTK:

python -m nltk.downloader all
python -m nltk.downloader wordnet
python -m nltk.downloader omw
python -m nltk.downloader sentiwordnet

Finally get spacy models:

python -m spacy download en_core_web_lg
python -m spacy download en_core_web_md
python -m spacy download en_core_web_sm
python -m spacy download it_core_news_sm
python -m spacy download de_core_news_sm
python -m spacy download fr_core_news_sm
python -m spacy download fr_core_news_md
python -m spacy download es_core_news_sm
python -m spacy download es_core_news_md
python -m spacy download nl_core_news_sm

Example

Let’s assume that you want to compute the sentiment associated to two tokens of interest, namely unemployment and economy, given the two following sentences.

text = ['Unemployment is rising at high speed', 'The economy is slowing down and unemployment is booming']
include = ['unemployment', 'economy']

get_sentiment(text = text, include = include)
>   Doc_id Text                       Chunk              Sentiment Tense Include  
>       1 Unemployment is rising at… Unemployment is r…    -0.899 pres… unemploy…
>       2 The economy is slowing do… economy is slowing    -0.3   pres… economy  
>       2 The economy is slowing do… unemployment is b…    -0.8   pres… unemploy…

The output of the function get_sentiment is a list, containing two objects:

• “sentiment” containing the average sentiment computed for each text;

• “sentiment_by_chunk” containing the sentiment computed for each chunk detected in the texts.

The first element of the output list provides the overall average sentiment score of each text, while the second provides the detailed score of each chunk of text that relates to one of the ToI.

Citations:

If you use this package, we encourage you to add the following references to the related papers:

• Barbaglia, L.; Consoli, S.; and Manzan, S. 2022. Forecasting with Economic News. Journal of Business and Economic Statistics, to appear. Available at SSRN: https://ssrn.com/abstract=3698121

• Consoli, S.; Barbaglia, L.; and Manzan, S. 2020. Fine-grained, aspect-based semantic sentiment analysis within the economic and financial domains. In Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020, 52 – 61.

• Consoli, S.; Barbaglia, L.; and Manzan, S. 2021. Explaining sentiment from Lexicon. In CEUR Workshop Proceedings, volume 2918, 87 – 95.

• Consoli, S.; Barbaglia, L.; and Manzan, S. 2022. Fine-grained, aspect-based sentiment analysis on economic and financial lexicon. Knowledge-Based Systems, 247: 108781