利用深度學習建立中文新聞情緒分類器

相關研究指出，新聞文字的情緒一直以來是扮演可能影響金融市場波動的角色之一。所以在每天千以萬計的新聞訊息之中，相較於傳統手工的方式，能夠有效快速地處理文字內容並且進行輿論導向分析，對市場交易走勢的後續預測與判斷是有幫助的。使用人工分類其缺點為成本高。故近年來，許多學者提出各種不同的新聞情緒分類器。在這些研究當中，大多利用新聞全文進行關鍵字的擷取並建立分類模型。因利用新聞全文之關鍵字進行新聞情緒判定會降低準確度，故本論文提出一個以句子為基礎的中文新聞情緒分類演算法。所提方法首先將收集的新聞進行斷句。接著，使用 TextRank 與 Word2Vec 進行關鍵句子的判定。所找出的關鍵句子進一步先透過財金新聞詞典進行情緒分數的計算用以判定每篇新聞的正負面情緒標籤，再用於產生句子的關鍵字，進而形成可用之訓練資料集。最後，透過長短期記憶深度學習與遞歸神經網路模型建立分類器。實驗數據部份，透過選用台灣二家股票公司近五年資料進行評估，結果顯示所提的方法是有效的。

關鍵字

中文文字探勘；長短期記憶；文本情緒分析

並列摘要

The literature indicates that news sentiment always has impact on the financial market. Thus, if news can be analyzed effectively, it will have benefit for the following trading. Traditionally, news sentiment is annotated by human, and the cost is high. Therefore, many researchers proposed different approaches for building classifiers for sentiment classification. Most of them are extracting key words from news, and the extracted key news are utilized to construct classifiers. However, using key words extracted from news may reduce the accuracy of the model, this thesis thus proposed a sentence-based Chinese news sentiment classification. It first divides news into sentences. Then, using the TextRank and Word2Vec, the key sentences are generated. By using the generated key sentences, the sentiment scores of them are calculated by comparing the existing financial lexicon to determine positive or negative sentiment of news, and the key words are also generated from key sentences to form training data set. At last, the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used to construct the news sentiment classification models. Experiments on a five-year real data set of two companies at Taiwan were made to evaluate the proposed models. The results indicate that the proposed approaches are effective.

並列關鍵字

Chinese news data mining ； LSTM ； GRU ； classification ； sentiment analysis

參考文獻

[1] P.Ekman, An argument for basic emotions, CognitionEmotion,vol.6, nos. 3–4, pp. 169–200, May 1992

Google Scholar

[2] L. S. Chen、H. R. Chiu，”Developing a Neural Network based Index for Sentiment Classification,”，International Journal of Advanced Information Technologies, Vol. 3, No. 2, pp. 15-35, 2009