Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations

AAAI Conferences

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query.  In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions.  The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity.  An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions --- this is the focus of our paper.  We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention.  We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets.  The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the top-level entity.  We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art.



Deeply Moving: Deep Learning for Sentiment Analysis

@machinelearnbot

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network.


Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification

AAAI Conferences

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classification in a target domain that has no or little supervised information. Existing cross-domain sentiment classification methods cannot automatically capture non-pivots, i.e., the domain-specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The proposed HATN provides a hierarchical attention transfer mechanism which can transfer attentions for emotions across domains by automatically capturing pivots and non-pivots. Besides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net aligning the non-pivots by using the pivots as a bridge. Specifically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P-net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.


Finding the right representation for your NLP data - Tryolabs Blog

@machinelearnbot

Let's look at sentiment analysis tools as an example. Expression of sentiment is a pragmatic phenomenon. To predict it correctly, we need to know both the meaning of the sentences and the context in which those sentences appeared. How do you get the meaning of a sentence? Well, you need to know the meaning of the lexical items and the sentence's syntactic structure.