四元组方法提升评论情感识别与评价：QPLSA

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在当前的IT领域，尤其是自然语言处理(NLP)和情感分析研究中，Aspect Level Sentiment Analysis (ALSA)是一项关键任务，它有助于从用户评论中提取和评估特定方面的情感倾向，对于市场分析、产品改进和消费者行为理解具有重要意义。本文探讨了"QPLSA: Utilizing quad-tuples for aspect identification and rating"这一创新方法，其核心在于解决以往ALSA方法中的局限性。传统ALSA方法主要依赖于二元组（head-modifier pairs），例如在评论如"nice room"中，"room"是主体（head），“nice”是修饰词（modifier）。这种处理方式忽略了实体（entity）和评级（rating）信息，这在实际应用中可能会影响情感分析的准确性。为了填补这个空白，研究者提出了一种新颖的四元组（quad-tuples）模型—— Quad-tuple PLSA（Probabilistic Latent Semantic Analysis with quad-tuples），它将实体、修饰词以及它们之间的关系（如情感极性）作为一个整体进行考虑。 QPLSA的主要贡献在于： 1. **扩展了数据结构**：通过引入四元组（entity, modifier, aspect, rating），模型能够捕捉到更丰富的语义信息，比如"room"与"nice"之间的关系以及整个评论的总体评价。 2. **考虑实体和评级**：以前忽视的实体和评级被整合进模型中，这使得分析更加全面，有助于识别出评论中不同实体的主观评价，比如房间类型（如家具、设施）或服务态度等。 3. **概率建模**：利用概率隐含语义分析技术，QPLSA可以处理大量文本数据，并通过统计学习来发现潜在的主题和情感模式，提高模型的预测性能。 4. **模型优化**：针对四元组数据的特性，可能需要对传统的PLSA算法进行适应性调整，比如改进主题模型或者引入新的协同过滤策略，以更好地适应复杂的情感表达。 5. **应用场景**：该方法在电商评论分析、产品反馈管理、品牌声誉监测等多个领域有广泛应用潜力，帮助企业及时了解用户的真实需求和满意度。 QPLSA作为一种创新的四元组分析方法，不仅提升了ALSA的精度，而且拓展了情感分析在实际业务中的应用范围。随着数据量的增长和技术的发展，未来的研究将继续探索如何更好地结合深度学习和多模态信息，以提升情感分析的智能水平。

2.1. Sentiment analysis at different levels

Previous work on sentiment analysis mainly focuses on document-level sentiment polarity categorization (Dave et al.,

2003; Pang et al., 2002) or product feature extraction (Popescu & Etzioni, 2005). Based on LDA (Blei et al., 2003) models, the

Joint Sentiment Topic (JST) in Lin and He (2011) is designed to mine review aspects at the document level, a similar work –

Aspect and Sentiment Uniﬁcation Model (ASUM) models the generative process for review documents (Jo & Oh, 2011). While

the bulk of such work focuses on the document level mining, some others address the sentiment analysis at the sentence level

(Yu & Hatzivassiloglou, 2003) or phrase level (Kim & Hovy, 2004; Takamura & Inui, 2007; Vasileios & McKeown, 1997).

Speciﬁcally, sentence-level sentiment analysis views each sentence as a processing unit. Bruce and Wiebe (1999) anno-

tated 1001 sentences as subjective or objective, and Wiebe, Bruce, and OHara (1999) described a sentence-level Naive Bayes

classiﬁer. Besides, LocalLDA (Brody & Elhadad, 2010) and SLDA (Jo & Oh, 2011) are implemented at the sentence level for

ﬁne-granularity aspect generation. An interesting work, the Multi-Grain Latent Dirichlet Allocation model (MG-LDA)

(Titov & McDonald, 2008a) represents documents as sets of sliding windows (containing several sentences), where they built

local and global topics for product feature extraction.

On the other hand, phrase-level sentiment analysis is attracting growing research interests. Morinaga et al. (2002) and

Nasukawa and Yi (2003) have already provided evidences that working at the expression level is of interest to consumers

of opinion-oriented information extraction. Another group of related work focuses on identifying a class of expressions,

and has been proved to be effective in polarity identiﬁcation for subjective expressions (Munson, Cardie, & Caruana,

2005; Riloff & Wiebe, 2003; Wilson et al., 2005). Pointed out in Wang et al. (2011), Baccianella et al. (2009), the bag-of-words

assumption seriously hampers the aspect identiﬁcation and rating accuracy of online reviews. With the increasing aware-

ness of ‘‘Feature-Opinion’’ pairs in review mining, a series of work Lu et al. (2009), Luo et al. (2012) are proposed at the

phrase level. In this paper, to extract ﬁne-grained product features, our approach is implemented utilizing quad-tuples of

(head, modiﬁer, rating, entity) at the phrase level.

2.2. Ratable aspect generation

Ratable aspect generation methods (topic-sentiment mixture models) aim to decompose the opinionated reviews into

aspects and analyze the opinions towards the aspects (Lu et al., 2009). Especially in recent years, Topic models

(Lakkaraju, Bhattacharyya, Bhattacharya, & Merugu, 2011; Lu et al., 2009 Mei et al., Mei, Ling, Wondra, Su, & Zhai, 2007;

Wang et al., 2010) have been applied to ratable aspect generation. Lu et al. (2009) adopted the unStructured and Structured

PLSA for aspect identiﬁcation, however, they did not consider rating or entity in the model generation stage. On the other

hand, LDA based methods, such as MG-LDA (Titov & McDonald, 2008b), LocalLDA (Brody & Elhadad, 2010) and SLDA (Jo

& Oh, 2011) are proposed for product feature extraction of different granularities. Unfortunately, all these methods are actu-

ally topic models rather than topic-sentiment mixture models, which only utilize word co-occurrences without incorporat-

ing sentiments (ratings/sentiment labels or opinion thesaurus).

Incorporating review rating into MG-LDA, the Multi-Aspect Sentiment model (MAS) (Titov & McDonald, 2008a) is pro-

posed to model topic-sentiment association. Mei et al. (2007) deﬁned the problem of topic-sentiment analysis on Weblogs

and proposed Topic-Sentiment Mixture (TSM) model to capture sentiments and extract topic life cycles. Wang et al. (2010)

proposed a rating regression approach for latent aspect rating analysis on reviews. One recent work Lakkaraju et al. (2011)

also focuses on sentence level topic-sentiment mixture models, where the facet coherence and sentiment coherence are

modeled as peer topics, and opinion words are adopted for sentiment modeling. Along this line of introducing sentiment

labels into topic models, the Joint Sentiment Topic (JST) (Lin & He, 2011) and the Aspect and Sentiment Uniﬁcation Model

(ASUM) (Jo & Oh, 2011) propose a new generative process of sentiments and topics. As a matter of fact, all the above men-

tioned approaches represent reviews as bag-of-words. The major difference of our model from these work is that our model

generates ratable aspects based on quad-tuples of (head, modiﬁer, rating, entity), i.e., bag-of-phrases.

3. Problem deﬁnition and preliminary knowledge

In this paper, our desideratum is to investigate the effectiveness of quad-tuple PLSA in review aspect mining. For com-

parison, a traditional 2-tuple PLSA–the Structured PLSA (Lu et al., 2009) is introduced. Moreover, the frequently used nota-

tions are summarized in Table 1. The relevant concepts are described in the following.

3.1. Problem deﬁnition

Phrase A phrase f ¼ðh; mÞ is a pair of head term h and modiﬁer m.

Quad-tuple A quad-tuple q ¼ðh; m; r ; eÞ is a vector of head term h, modiﬁer m, rating r and entity e. Given a review on

entity e with rating r, we can generate a set of quad-tuples, denoted by {(h,m,r,e)jPhrase f appears with rating r in a review

of entity e}.

Aspect Cluster An aspect cluster A

is a cluster of head terms which share similar meaning in the given context. We rep-

resent A

¼fhjGðhÞ¼ig, where G is a mapping function that maps h to a cluster aspect A

28 W. Luo et al. / Information Processing and Management 51 (2015) 25–41

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四元组方法提升评论情感识别与评价：QPLSA

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