第五章、 研究結論與建議
2、 未來研究建議
國
立 政 治 大 學
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Na tiona
l Ch engchi University
2、 未來研究建議
針對本研究的問題與不足的部份進一步發展，可分為下列幾點：
（1） 在情感傾向判斷中，本研究只使用狀態類述詞來建立情感詞集，因而產 生負面評論判斷效果不佳。未來在針對建立情感詞集的部份，可加入具負面 傾向的體詞，或是在計算情感分數時，將經否定詞修飾的動作類述詞納入計 算考量。
（2） 承第一點，在建立判斷情感傾向所需要詞集時，可使用已存在的詞庫資 源，如 HowNet 和 NTUSD，並嘗試透過已存在的詞庫資源結合中文詞彙網 路來建立擴增的詞集。
（3） 在議題擷取中，使用 Kmeans 和 SOM 配合 TF-IDF 模型比起利用 NPMI 模型結合社群網路分析效果還來的差。其中 NPMI 為衡量字詞之間強度的分 數，故未來可嘗試將 Kmeans 和 SOM 並在距離函數採用相關性計算的方式，
結合 NPMI 模型來對議題字分群。
（4） 承第四點，本研究採用社群網路分析來完成分群再進行合併，在分類模 型的分類結果雖然表現不錯，但在合併過程仍需要透過人為整調分群的結果，
因此只能達成半自動的方式，要如何進一步的降低人為介入的程度，可納入 不同的研究方法來改善。
（5） 本研究的資料來源僅使用三個月的評論(1/1 到 3/31)，在視覺化分析的 部份可從對應分析看出三個月內的短期影響，為了實際試驗對應分析在長時 間的競爭關係，可擴大實驗採用評論的時間區間。
（6） 透過對應分析的視覺化方法可呈現 App 的市場定位，進一步可將對應 分析使用在不同商品評論中，以消費者的角度分析出不同商品的市場定位，
可提供商品開發的建議與改良。
‧ 國
立 政 治 大 學
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Na tiona
l Ch engchi University
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