Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Recent origin of a hominoid-specific splice form of neuropsin, a gene involved in learning and memory

Neuropsin is a secreted-type serine protease involved in learning and memory. The type II splice form of neuropsin is abundantly expressed in the human brain but not in the mouse brain. We sequenced the type II-spliced region of neuropsin gene in humans and representative nonhuman primate species. Our comparative sequence analysis showed that only the hominoid species (humans and apes) have the intact open reading frame of the type II splice form, indicating that the type II neuropsin originated recently in the primate lineage about 18 MYA. Expression analysis using RT-PCR detected abundant expression of the type II form in the frontal lobe of the adult human brain, but no expression was detected in the brains of lesser apes and Old World monkeys, indicating that the type II form of neuropsin only became functional in recent time, and it might contribute to the progressive change of cognitive abilities during primate evolution.

A human-specific mutation leads to the origin of a novel splice form of neuropsin (KLK8), a gene involved in learning and memory

Neuropsin (kallikrein 8, ELKS) is a secreted-type serine protease preferentially expressed in the central nervous system and involved in learning and memory. Its splicing pattern is different in human and mouse, with the longer form (type II) only express

Functional characterization of the human-specific (type II) form of kallikrein 8, a gene involved in learning and memory

Kallikrein 8 (KLK8) is a serine protease functioning in the central nervous system, and essential in many aspects of neuronal activities. Sequence comparison and gene expression analysis among diverse primate species identified a human-specific splice for

A dichotomic search algorithm for mining and learning in domain-specific logics

Ferr?, S. and King, R. D. (2004) A dichotomic search algorithm for mining and learning in domain-specific logics. Fundamenta Informaticae. IOS Press. To appear

Task-Irrelevant Perceptual Learning Specific to the Contrast Polarity of Motion Stimuli

Studies of perceptual learning have focused on aspects of learning that are related to early stages of sensory processing. However, conclusions that perceptual learning results in low-level sensory plasticity are of great controversy, largely because such learning can often be attributed to plasticity in later stages of sensory processing or in the decision processes. To address this controversy, we developed a novel random dot motion (RDM) stimulus to target motion cells selective to contrast polarity, by ensuring the motion direction information arises only from signal dot onsets and not their offsets, and used these stimuli in conjunction with the paradigm of task-irrelevant perceptual learning (TIPL). In TIPL, learning is achieved in response to a stimulus by subliminally pairing that stimulus with the targets of an unrelated training task. In this manner, we are able to probe learning for an aspect of motion processing thought to be a function of directional V1 simple cells with a learning procedure that dissociates the learned stimulus from the decision processes relevant to the training task. Our results show learning for the exposed contrast polarity and that this learning does not transfer to the unexposed contrast polarity. These results suggest that TIPL for motion stimuli may occur at the stage of directional V1 simple cells.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

Procedural learning deficits in specific language impairment (SLI): a meta-analysis of serial reaction time task performance

Meta-analysis and meta-regression were used to evaluate whether evidence to date demonstrates deficits in procedural memory in individuals with specific language impairment (SLI), and to examine reasons for inconsistencies of findings across studies. The Procedural Deficit Hypothesis (PDH) proposes that SLI is largely explained by abnormal functioning of the frontal-basal ganglia circuits that support procedural memory. It has also been suggested that declarative memory can compensate for at least some of the problems observed in individuals with SLI. A number of studies have used Serial Reaction Time (SRT) tasks to investigate procedural learning in SLI. In this report, results from eight studies that collectively examined 186 participants with SLI and 203 typically-developing peers were submitted to a meta-analysis. The average mean effect size was .328 (CI95: .071, .584) and was significant. This suggests SLI is associated with impairments of procedural learning as measured by the SRT task. Differences among individual study effect sizes, examined with meta-regression, indicated that smaller effect sizes were found in studies with older participants, and in studies that had a larger number of trials on the SRT task. The contributions of age and SRT task characteristics to learning are discussed with respect to impaired and compensatory neural mechanisms in SLI.

A newly recognized syndrome of marfanoid habitus; Long face; Hypotelorism; Long, thin nose; Long, thin hands and feet; and a specific pattern of language and learning disabilities

Here, we report on a newly recognized syndrome in a Brazilian family with three affected women, who had a Marfanoid habitus; long face; hypotelorism; long, thin nose; long, thin hands and feet; and language and learning disabilities. The disorder is compatible with autosomal dominant inheritance. (C) 2007 Wiley-Liss, Inc.