Implementation

Implementation#

Aspect-Based Sentiment Analysis (ABSA) has emerged as a critical tool in understanding public sentiment in granular detail. Given its applicability in various domains such as finance, healthcare, and reputation management, the need for robust ABSA frameworks is evident. This section delineates the steps for constructing an ABSA pipeline that leverages the generative pre-trained model GPT-4. The work aims to provide a detailed, step-by-step methodology that focuses on using this generative model to analyze sentiment related to distinct aspects of companies and their owners.

Phase 1: Data Preparation#

Data Collection#

For a robust ABSA model, the quality of data is paramount. Web scraping algorithms target the Daum news website to harvest articles specifically discussing companies and their owners. The chosen website provides a rich corpus focusing on relevant aspects like “management,” “financial health,” and “corporate social responsibility,” among others.

Data Annotation#

After data acquisition, a team of domain experts annotates the text corpus assisted by a prompt-guided labeling system. Aspects such as “management” and “financials” are labeled based on pre-defined prompts. This guided annotation aims to provide a structured, standardized dataset, thereby reducing the scope of labeling errors and facilitating easier modeling downstream.

Data Cleaning#

Text normalization techniques convert the corpus to a standard form, including lowercasing, stripping special characters, and handling whitespace. Irrelevant data points that don’t align with the study objectives are purged to concentrate the dataset.

Train-Test Split#

Stratified sampling ensures each subset—training, validation, and test—is a representative microcosm of the overall dataset. This stratification balances the aspects and sentiments across all subsets, enabling a fair evaluation of model performance.

Phase 2: Aspect Extraction#

Model Choice and Fine-Tuning#

GPT-4 is selected for its proven capabilities in complex NLP tasks. An objective function is customized to extend the model’s capabilities from text generation to aspect identification.

\[ \max_{\theta} \sum_{i=1}^{N} log P(x_i, y_i | x_{<i}; \theta) \]

Training and Validation#

The model is trained on the annotated training set using Large Language Model (LLM) embeddings. These embeddings enable classifiers to understand nuanced language features that simple word embeddings might miss. Performance metrics like F1-score, precision, and recall are monitored during validation to tune the model parameters.

Phase 3: Aspect Sentiment Analysis#

Additional Fine-Tuning#

The model undergoes a second fine-tuning stage to predict the sentiment polarity for each identified aspect. The objective function used for this phase is:

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\[ \max_{\theta} \sum_{i=1}^{N} log P(x_i, y_i, z_i | x_{<i}, y_{<i}; \theta) \]

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Training and Validation#

Here, LLM embeddings remain instrumental in training classifiers that can accurately predict sentiment polarity.

Phase 4: Post-Processing#

Sentiment Aggregation#

For recurring aspects, sentiments are statistically aggregated using methods like mode or mean to generate a single sentiment score. This aggregation aids in summarizing the general sentiment concerning specific aspects.

Output Formatting#

Finally, the extracted insights are formatted into JSONlines format, enabling both human interpretation and machine parsing.

Sample Output

[
    {"company": "카카오", "aspect": "product & service", "aspect_terms": ["데이터센터 화재에 따른 먹통 사태", "데이터센터 화재에 따른 서비스 장애 피해지원 계획", "이모티콘 총 종 제공", "매출 손실 규모액에 따른 지원금 지급"], "opinion_terms": ["보상안 발표", "다양한 단체와 협의체를 구성하고 논의를 지속", "직접적인 피해가 큰 경우만 별도 과정을 거쳐 개별 지원", "사회적 책임 차원의 일괄 지원을 결정", "안정적인 서비스를 제공하겠다는 약속"], "sentiment": "positive"},
    {"company": "넥슨", "aspect": "social", "aspect_terms": ["김정주 전 회장의 가상자산 계좌가 해킹", "억원어치 가상자산이 도난당한 사실"], "opinion_terms": ["사망자의 가상자산은 제도가 없어 보호받지 못한다는 지적", "논란이 일었다"], "sentiment": "negative"},
    …
]

Conclusion and Alternative Solutions#

The paper describes an ABSA implementation using GPT-4, providing a nuanced understanding of sentiments related to different aspects of companies and their owners. Future iterations could explore the utilization of other generative models or include multilingual support for enhanced scalability. Automated annotation techniques could also be integrated to expedite the data preparation phase. By incorporating these elements, the model could offer even more comprehensive insights, further enriching reputation management tactics.

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