K-Means for Search Results Clustering Using URL and Tag Contents

Poomagal, S.; Hamsapriya, T.

doi:10.1109/pacc.2011.5978906

Cited by 6 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Words conveying low meanings (e.g. "and" or "the") to which no particular sentiment may be associated are known as stop words [47]− [49]. As a result, stop words are eliminated from the document set too.…”

Section: Preprocessingmentioning

confidence: 99%

Patient-patient interactions visualization for drug side effects in patients’ reviews

Salah,

Elsoud,

Salah

et al. 2024

IJEECS

View full text Add to dashboard Cite

This paper describes the patient-patient interactions (PPIs) graph extraction framework from patient’s review transcripts. The concept is to visualise patients as nodes and interactions representing links. Links are made based on review text similarity. Nodes are categorized as positive or negative according to the patient’s attitude toward a given drug. Attitudes are then utilized to categorize the links as supporting or opposing the use of a certain drug. If both patients share the same attitude: negative (severe side effect) or positive (moderate side effect), the relationship is considered supportive; if not, the link is considered opposed. Resulting graph represent a drug as a dispute between two factions arguing on related drug. The framework is explained and evaluated using a dataset included 3,763 patients’ reviews linked to 255 different drugs, -predictive-value (0.37). We argue that, this is caused by derogatory jargon that is an expected feature of patient’s review. The true-negative-recognition-rate is 0.70 and true-positive-recognition-rate is 0.54. Total-average-accuracy, which is independent of class priors, is 0.66. Results show that, it is possible to use text proximity measures and sentiment analysis to capture PPIs structure.

show abstract

Section: Preprocessingmentioning

confidence: 99%

Patient-patient interactions visualization for drug side effects in patients’ reviews

Salah,

Elsoud,

Salah

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…0.48% in view of the keywords "alcoholism" and "local computer shop" on data of 300 students. Identical group formed from similar objects from a set of objects which is called as Cluster Analysis [18]. Such indistinguishable grouping of resembling data is termed clusters.…”

Section: Literature Surveymentioning

confidence: 99%

Research on Mechanism and Challenges in Meta Search Engines

2019

IJITEE

View full text Add to dashboard Cite

A Meta Search Engine (MSE) produces results gathered from other search engine (SE) on a given query. In brief MSEs have single interface corresponding to multiple searches. MSE employs their own algorithm to display search results. This paper reviews existing Meta Search Engines like Yippy, eTools.ch, Carrot2, qksearch and iBoogie commonly used for searching. This paper surveys and analysed the working of different result merging algorithms. Current research reviews MSE based on different approaches like clustering technique. Few MSEs are employing Neural networks for searching.

show abstract

“…Stop words are words that carry little meaning (such as "and" or "the") to which no particular sentiment can be attached [7,13,23], stop words are thus removed from the document set. Given a specific domain there will also be additional words, other than stop words, that occur frequently.…”

Section: Preprocessingmentioning

confidence: 99%

Extracting debate graphs from parliamentary transcripts

Salah

Coenen

Grossi

2013

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law

View full text Add to dashboard Cite

The paper proposes a framework-the Debate Graph Extraction (DGE) framework-for extracting debate graphs from transcripts of political debates. The idea is to represent the structure of a debate as a graph with speakers as nodes and "exchanges" as links. Links between nodes are established according to the semantic similarity between the speeches and indicate an alignment of content between them. Nodes are labelled according to the "attitude" (sentiment) of the speakers, positive or negative, using a lexicon based technique founded on SentiWordNet. The attitude of the speakers is then used to label the graph links as being either "supporting" or "opposing". If both speakers have the same attitude (both negative or both positive) the link is labelled as being supporting; otherwise the link is labelled as being opposing. The resulting graphs capture the abstract representation of a debate as two opposing fractions exchanging arguments on related content.

show abstract

K-Means for Search Results Clustering Using URL and Tag Contents

Cited by 6 publications

References 6 publications

Patient-patient interactions visualization for drug side effects in patients’ reviews

Patient-patient interactions visualization for drug side effects in patients’ reviews

Research on Mechanism and Challenges in Meta Search Engines

Extracting debate graphs from parliamentary transcripts

Contact Info

Product

Resources

About