Text Representation Using Dependency Tree Subgraphs for Sentiment Analysis

Pak, Alexander; Paroubek, Patrick

doi:10.1007/978-3-642-20244-5_31

Cited by 20 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reason for selecting a sample of 1000 is that human annotation is a manual and time-intensive task. Similar research into the classification of emotive texts using a human annotated gold-standard has typically used a sample of 1000 to good effect [24] , [25] , [26] , [27] .…”

Section: The Collection Of Twitter Datamentioning

confidence: 99%

Analysing the connectivity and communication of suicidal users on twitter

Colombo

Burnap

Hodorog

et al. 2016

Computer Communications

117

View full text Add to dashboard Cite

HighlightsWe investigate the characteristics of the authors of Tweets containing suicidal intent or thinking, through the analysis of their online social network relationships and interactions.Results show a high degree of reciprocal connectivity between the authors of suicidal content when compared to other studies of Twitter users, suggesting a tightly-coupled virtual community.Analysis of the retweet graph identified bridge nodes and hub nodes connecting users posting suicidal ideation with users who were not, suggesting a potential for information cascade and risk of possible ‘contagion’.Retweet graphs of suicidal content exhibit an average shortest path similar to that of a large comparison network, demonstrating large scale information propagation in small-scale networks.

show abstract

Section: The Collection Of Twitter Datamentioning

confidence: 99%

Analysing the connectivity and communication of suicidal users on twitter

Colombo

Burnap

Hodorog

et al. 2016

Computer Communications

117

View full text Add to dashboard Cite

show abstract

“…All the parsers output in an adapted CoNLL 6 data format. Once we have the parsed output 7 we can extract the patterns and start experimenting.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Our patterns are dependency sub-trees obtained by generating sub-trees of all possible depth using either or both lemma and POS features. The use of sub-trees for text representation can be found in [7]. the significant features of our sub-tree patterns are, These patterns were extracted (as in [8]) automatically and since we focused on the dependency structure of a sentence, less significant tokens (e.g.…”

Section: Dependency Sub-tree Patternsmentioning

confidence: 99%

Using Verb-Noun Patterns to Detect Process Inputs

Asadullah

Nouvel

Paroubek

2014

Text, Speech and Dialogue

Self Cite

View full text Add to dashboard Cite

Abstract. We present the preliminary results of an ongoing work aimed at using morpho-syntactic patterns to extract information from process descriptions in a semi-supervised manner. The experiments have been designed for generic information extraction tasks and evaluated on detecting ingredients from cooking recipes in French using a large gold standard corpus. The proposed method uses bi-lexical dependency oriented syntactic analysis of the text and extracts relevant morpho-syntactic patterns. Those patterns are then used as features for different machine learning methods to acquire the final ingredient list. Furthermore, this approach may easily be adapted to similar tasks since it relies on mining generic morpho-syntactic patterns from the documents automatically. The method itself is language independent, considering language specific parsers being used. The performance of our method on the DEFT 2013 data set is nevertheless satisfactory since it significantly outperforms the best system from the original challenge (0.75 vs 0.66 MAP).

show abstract

“…Pak and Paroubek used subgraphs extracted from the dependency tree of a parsed sentence for constructing the feature vector [12]. They performed experimentations on movie reviews and decided that the subgraph-based features along with SVM classifier gave the best performance.…”

Section: Related Workmentioning

confidence: 99%

Use of NLP Based Combined Features for Sentiment Classification

Kalaivani¹,

Kuppuswami²

2019

IJEAT

View full text Add to dashboard Cite

Sentiment analysis is the technique of automatic detection of the belief or the mood of an author towards a certain subject in textual form. To extract the opinion present in text, the machine needs expertise in the area of natural language processing. In this paper, machine learning based document-level sentiment classification is performed on Amazon product reviews to classify them as positive and negative. Two NLP based feature extraction techniques (Word Relation and POS based) are used in this study to determine the features that are sentiment bearing. The features are extracted as basic features (unigrams, bigrams and trigrams) and their combinations (unigrams+bigrams, unigrams+trigrams, unigrams+bigrams+trigrams). In order to identify the features that are most informative and to bring down the computational time of the classification algorithms, feature selection techniques are used. Performance of independent and combined feature sets is assessed using accuracy, precision, recall and F-measure. From the experiments conducted, it is observed that combined features outperformed independent features using Boolean Multinomial Naive Bayes (BMNB) classifier.

show abstract

Text Representation Using Dependency Tree Subgraphs for Sentiment Analysis

Cited by 20 publications

References 6 publications

Analysing the connectivity and communication of suicidal users on twitter

Analysing the connectivity and communication of suicidal users on twitter

Using Verb-Noun Patterns to Detect Process Inputs

Use of NLP Based Combined Features for Sentiment Classification

Contact Info

Product

Resources

About