Semantic Outlier Detection

نویسندگان

  • Erik Cambria
  • Giuseppe Melfi
چکیده

Between the dawn of the Internet through year 2003, there were just a few dozens exabytes of information on the Web. Today, that much information is created weekly. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised increasing interest both in the scientific community, for the exciting open challenges, and in the business world, for the remarkable fallouts in social media marketing and financial forecast. Keeping up with the ever-growing amount of unstructured information on the Web, however, is a formidable task. Unlike standard statistical approaches, sentic computing relies on a vector space model of affective common-sense knowledge to work with natural language at conceptlevel. The well-known noisiness of common-sense data sources, however, is a major factor in jeopardizing the efficiency of analogical reasoning in the vector space. In this work, it is explored how least absolute deviations can aid semantic outlier detection and, hence, enhance concept-level opinion mining.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semantic Integrity Constraint Rule Discovery and Outlier Detection in Relational Data as a Data Quality Mining Technique

Data Quality is critical to the quality of patterns and analysis obtained from data. One of the important factors plaguing data is violation of Semantic Integrity, leading to inconsistency, in turn resulting in generation of bad patterns or reports when data mining or warehousing techniques are applied on such data. In this paper, a data quality mining technique is proposed to automatically gen...

متن کامل

Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving Object Trajectories

Several algorithms have been proposed for discovering patterns from trajectories of moving objects, but only a few have concentrated on outlier detection. Existing approaches, in general, discover spatial outliers, and do not provide any further analysis of the patterns. In this paper we introduce semantic spatial and spatio-temporal outliers and propose a new algorithm for trajectory outlier d...

متن کامل

Outlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis

Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be ca...

متن کامل

Outlier Detection Using SemiDiscrete Decomposition

Semidiscrete decomposition (SDD) is usually presented as a storage-eecient analogue of singular value decomposition. We show, however, that SDD actually works in a completely diierent way, and is best thought of as a bump-hunting technique; it is extremely eeective at nding outlier clusters in datasets. We suggest that SDD's success in text retrieval applications such as latent semantic indexin...

متن کامل

Detecting Errors in Numerical Linked Data Using Cross-Checked Outlier Detection

Outlier detection used for identifying wrong values in data is typically applied to single datasets to search them for values of unexpected behavior. In this work, we instead propose an approach which combines the outcomes of two independent outlier detection runs to get a more reliable result and to also prevent problems arising from natural outliers which are exceptional values in the dataset...

متن کامل

Outlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means

One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015