Closed farinamhz closed 1 year ago
This paper's main problem is detecting messages' aspects as a sub-task of Aspect-based Sentiment Analysis. Their purpose is to report an extraction method of financial aspects in microblog messages. In fact, they did this work in the new domain, which is finance and economics.
Existing methods for detecting aspects can be divided into two categories:
Unsupervised: All the unsupervised methods use lexicons to search for explicit words linked to aspects. These lexicon-based approaches rely on three things :
Supervised: These approaches rely on machine learning algorithms that are trained on the instances with labels as aspects and then tested on new instances. While many studies have proposed different types of Conditional Random Fields (CRF) models that differentiate between aspects and non-aspects in text sequences without paying attention to the correlation of the words, other methods identify on the basis of predefined aspects linked to the entity and attribute pairs.
Besides, traditional approaches have focused on explicit aspects without paying attention to implicit ones. So, they focus on word occurrences to determine aspects. However, newer approaches focus on identifying aspects that are implicitly referred to. For instance, there is work done by Dosoula et al. in 2016, who developed an implicit feature algorithm that uses cooccurrences to assign implicit aspects at the sentence level in online restaurant reviews.
For example, in â$MCD with declining revenue for a good while,â âdeclining revenueâ is extracted, which is relevant for the classification as âRevenue Down.â Also, âRevenue Downâ is a sub-category of âFinancial Results.â
The paper presents two approaches:
1- Distributional Semantics Model (DSM) In this model, basically, they use word embeddings to compute semantic relatedness. This will be done in two steps: Firstly, they use morpho-syntactic patterns to select relevant noun and verb phrases in extracting candidates. Secondly, in computing the relatedness of candidates with the classes, they combine the multi-word candidates into single vectors by getting the sum of the vectors, then they compute the cosine similarity for all possible pairs of tokens in each message and hold the highest score.
2- Supervised Machine Learning Models In this approach, we face a multi-class supervised classification problem that has feature engineering, machine learning algorithm optimization, and model selection and evaluation stages:
Dataset
They have used a corpus of messages specialized in stock trading, which traders posted. This dataset contains 218 messages containing implicit aspects and 150 messages containing explicit aspects. The messages were manually classified by one financial expert, according to the taxonomy that they provided in the appendix section, by matching aspect classes and subclasses with messages. The annotated dataset explained above has been used as the training and test dataset.
Evaluation and Metrics
In the model selection step, they computed global accuracy for 32 classes. However, in the validation step, they used F1-Score for 7 and 32 classes to measure the effects of the coarse and fine-grained annotation levels.
In the model selection step, XGBoost scored the highest accuracy and was selected. The figure below shows the results of XGBoost on aspect classes and aspect subclasses classification, as well as implicit and explicit aspect classification. XGBoost scored 71% accuracy on the 7-aspect class classification, 82% on explicit aspect classification, and 35% on implicit aspect classification.
https://github.com/DeepthiSudharsan/Aspect-Extraction-of-Financial-Microblogs (Unofficial)
No presentation was provided.
Explicit aspect classification performed well, but the results of implicit aspect classification would have been better. However, they stated that it could be tackled with a larger dataset and better feature engineering. In general, their method is straightforward and needs too many datasets. Also, it was not a new approach, just doing the previous ones (kind of a survey) and testing in the financial domain.
Implicit and Explicit Aspect Extraction in Financial Microblogs
I chose this paper because they stated in the subject of the paper that they have focused on implicit aspects.