Semantic Multi-Dimensional Scaling for Open-Domain Sentiment Analysis

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Cambria, E., Song, Y., Wang, H., & Howard, N. (2013). Semantic Multi-Dimensional Scaling for Open-Domain Sentiment Analysis. IEEE Intelligent Systems, 99(1), 1.


This work presents an approach for blending ProBase, an existing taxonomy of common knowledge, with ConceptNet, a common-sense knowledge base. Afterwards, multi-dimensional scaling (MDS) is applied to perform sentiment analysis with the created knowledge base.

  • ProBase contains a probabilistic taxonomy with about 12 million concepts which have been learned based on 1.68 billion Web pages in the Bing repository.
  • ConceptNet is a common-sense semantic network which contains 173,398 nodes and has been created out of facts collected by the Open Mind Common Sense (OMCS) project.


  1. Create the Isanette knowledge source which (a) is build from ProBase isA links, (b) deduplicates concepts with a high similarity according to MDS and a high word similarity, (c) removes hapax legomena (nodes with singular out-/in-degrees) and nodes with a connectivity of less than 10, (d) use MDS to infer negative evidence such as 'carbonara' is no food and 'alitalia' is not country.
  2. Blend Isanette with ConceptNet using the blending technique introduced by Havasi et al. (2009) to create IsaCore, a strongly-connected core of common and common-sense knowledge.
  3. Reasoning on the knowledge base:
    • apply singular value decomposition (SVD) to build a lower-dimensional (500 dimensions) vector space representation of the instance-concept relationship matrix obtained from IsaCore. The obtained concept matrix $$\tilde{C}_U$$ does not yield meaningful affective results, since it only considers semantic relatedness of instances according to isA relations.
    • use semi-supervised linear discriminant analysis (LDA) with affective instances $$e_i \in \mathcal{R}^d$$ and label $$y_i \in \{1, ...q\}$$ to find a projection matrix W which projects the semantic space $$\tilde{C}_U$$ to a lower-dimensional space which is more affectively discriminative.
    • employ the sentic medoids approach to semantically cluster $$\tilde{C}_U$$ into k distinct categories which represent Isanette's hub concepts, i.e. the 5000 concepts with the highest in-degree.

  4. The authors use the created knowledge base in a four step sentiment analysis approach
    • pre-processing (negation, degree adverbs, emoticons, lemmatization...)
    • semantic parsing to extract concepts from opinionated text (extracts small bags of concepts from the text; normalization - i.e. 'buy christmas present'~ 'I bought a lot of very nice Christmas presents')
    • target spotting to identify potential targets
    • affect interpretation for emotion recognition and polarity detection (project the extracted concepts into $$\tilde{C}_U$$ clustered according to the Hourglass labels and compute the polarity based on $$ p = \sum_{i=1}^N \frac{Plsnt(c_i) + |Attnt(c_i)| - |Snst(c_i)| + Aptit(c_i)}{3N}$$ where N is the size of the bag of concepts and 3 the normalization factor.


The evaluation draws upon the following three corpora:

  1. Twitter hashtag repository
  2. LiveJournal database which allows bloggers to label their posts with mood tags
  3. a manually annotated dataset of 2000 patient opinions
It then evaluates (i) the spotting precision of topics (electronics, companies, etc.) on the Twitter repository, (ii) the recognition of affective pairs from the Hourglas model (Pleasantness: joy-sadness; Attention: anticipation-surprise; Sensitivity: anger-fear; Aptitude: trust-disgust), and (iii) the classification of patient opinions.