近年來隨著智慧型手機的普及以及社群網站的盛行，使得人們比起在傳統部落格上更常在社群網路上發言，藉由這些服務來快速分享他們的生活以及評論，因此社群網站的內容成為是一個非常好分析使用者對產品使用心得的資料來源。然而，人們在社群網站上的發言內容多半非常的短，而且用字遣詞也比較不完整，其中還夾雜了表情符號、標記、連結等。另外，產品特徵的表示方法也較於多變。因此，在產品特徵聚合的議題上，資訊稀疏的問題往往造成語意分析上的不準確，使得特徵詞在聚合的過程中更加困難。
為了解決上述的問題，我們針對推特上的產品特徵聚合提出了一個漸進式的語意強化方法。我們加入了字面特徵來輔助一開始語意特徵不精確的問題，藉由聚合過程中當下子概念群的狀態回饋，不斷整合相關的資訊去強化語意特徵，使語意特徵更加的精確來達到產品特徵聚合的準確。根據我們的研究結果顯示我們的方法相較於缺其一條件或是單純使用語意特徵，其結果相對都來得好。藉此證明，透過加入其他特徵，並以漸進式的方式來整合資訊，對於在資料稀疏的狀態下了解語意是有幫助的。

In recent years, with the popularity of smartphones and the prevalence of social networking services, people more often share their lives and comments quickly in these services than in the traditional blog websites. Therefore, social networking services are the valuable sources for the product feature opinion mining. However, generally the “quick” comments are quite short, lack complete grammatical and syntactical structures; even they are full of abbreviations, irregular expressions, and URLs etc. In addition, their product feature expressions are more diverse. Thus, the information sparse problem will lead to more difficulty in the product feature grouping
In order to solve the problems above, we propose a progressive semantic reinforcement approach for product feature grouping in Twitter. We combine the literal characteristics to help the semantic characteristics which are rough initially and then aggregate the related information progressively through the sub-concept group information to enhance the semantic characteristics for achieving the better grouping results. Our experimental results show that our approach is better than the ones which only use one of the conditions above or neither. These results prove that adding other characteristics and aggregating information progressively can be helpful for understanding the semantics in the sparse information.

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