Sentiment Learning on Product Reviews via Sentiment Ontology Tree
Wei, Wei and Gulla, Jon Atle

Article Structure

Abstract

Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized.

Introduction

As the internet reaches almost every corner of this world, more and more people write reviews and share opinions on the World Wide Web.

Related Work

The task of sentiment analysis on product reviews was originally performed to extract overall sentiment from the target texts.

The HL-SOT Approach

In this section, we first propose a formal definition on SOT.

Empirical Analysis

In this section, we conduct systematic experiments to perform empirical analysis on our proposed HL-SOT approach against a human-labeled data set.

Conclusions, Discussions and Future Work

In this paper, we propose a novel and effective approach to sentiment analysis on product reviews.

Topics

sentiment analysis

Appears in 33 sentences as: Sentiment Analysis (1) sentiment analysis (32)
In Sentiment Learning on Product Reviews via Sentiment Ontology Tree
  1. Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized.
    Page 1, “Abstract”
  2. While this paper is mainly on sentiment analysis on reviews of one product, our proposed HL-SOT approach is easily generalized to labeling a mix of reviews of more than one products.
    Page 1, “Abstract”
  3. Faced with this problem, research works, e.g., (Hu and Liu, 2004; Liu et al., 2005; Lu et al., 2009), of sentiment analysis on product reviews were proposed and have become a popular research topic at the crossroads of information retrieval and computational linguistics.
    Page 1, “Introduction”
  4. Carrying out sentiment analysis on product reviews is not a trivial task.
    Page 1, “Introduction”
  5. We believe that labeling existing product reviews with attributes and corresponding sentiment forms an effective training resource to perform sentiment analysis .
    Page 1, “Introduction”
  6. We argue that when performing sentiment analysis on reviews, such as in the Example 1, more attention is needed to distinguish between attributes that are mentioned with and without sentiment.
    Page 2, “Introduction”
  7. In this paper, we study the problem of sentiment analysis on product reviews through a novel method, called the HL—SOT approach, namely Hierarchical Learning (HL) with Sentiment Ontology Tree (SOT).
    Page 2, “Introduction”
  8. By sentiment analysis on product reviews we aim to fulfill two tasks, i.e., labeling a target text1 with: l) the product’s attributes (attributes identification task), and 2) their corresponding sentiments mentioned therein (sentiment annotation task).
    Page 2, “Introduction”
  9. With the proposed concept of SOT, we manage to formulate the two tasks of the sentiment analysis to be a hierarchical classification problem.
    Page 2, “Introduction”
  10. 0 To the best of our knowledge, with the proposed concept of SOT, the proposed HL—SOT approach is the first work to formulate the tasks of sentiment analysis to be a hierarchical classification problem.
    Page 2, “Introduction”
  11. further proposed to achieve tasks of sentiment analysis in one hierarchical classification process.
    Page 3, “Introduction”

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learning algorithm

Appears in 8 sentences as: Learning Algorithm (1) learning algorithm (7)
In Sentiment Learning on Product Reviews via Sentiment Ontology Tree
  1. We further propose a specific hierarchical learning algorithm , called HL-SOT algorithm, which is developed based on generalizing an online-leaming algorithm H-RLS (Cesa—Bianchi et al., 2006).
    Page 2, “Introduction”
  2. 0 A specific hierarchical learning algorithm is
    Page 2, “Introduction”
  3. In (Turney, 2002), an unsupervised learning algorithm was proposed to classify reviews as recommended or not recommended by averaging sentiment annotation of phrases in reviews that contain adjectives or adverbs.
    Page 3, “Related Work”
  4. Then a specific hierarchical learning algorithm is further proposed to solve the formulated problem.
    Page 4, “The HL-SOT Approach”
  5. Therefore we propose a specific hierarchical learning algorithm , named HL-SOT algorithm, that is able to train each node classifier in a batch-learning setting and allows separately learning for the threshold of each node classifier.
    Page 5, “The HL-SOT Approach”
  6. Then the hierarchical classification function f is parameterized by the weight matrix W = (7.01, ..., wN)T and threshold vector 6 = (61, ..., 6N)T. The hierarchical learning algorithm HL-SOT is proposed for learning the parameters of W and 6.
    Page 5, “The HL-SOT Approach”
  7. Algorithm 1 Hierarchical Learning Algorithm HL—SOT
    Page 6, “The HL-SOT Approach”
  8. The hierarchial learning algorithm HL—SOT is presented as in Algorithm 1.
    Page 6, “The HL-SOT Approach”

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weight vector

Appears in 3 sentences as: weight vector (2) weight vectors (1)
In Sentiment Learning on Product Reviews via Sentiment Ontology Tree
  1. Defining the f function Let wl, ..., 212 N be weight vectors that define linear-threshold classifiers ofeach node in SOT.
    Page 5, “The HL-SOT Approach”
  2. The Formula 1 restricts that the weight vector wig; of the classifier i is only updated on the examples that are positive for its parent node.
    Page 5, “The HL-SOT Approach”
  3. In the training process of HL-flat, the algorithm reflexes the restriction in the HL-SOT algorithm that requires the weight vector wig; of the classifier i is only updated on the examples that are positive for its parent node.
    Page 7, “Empirical Analysis”

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