Aleksander Bai

A Model-Based Admission Control for 802.11e EDCA using Delay Predictions

This paper presents a unique approach for a model-based admission control algorithm for the IEEE 802.11e enhanced distributed channel access (EDCA) standard. The analytical model used as the foundation for the algorithm covers both nonsaturation and saturation conditions. This allows us to keep the system out of saturation by monitoring several variables. Since the medium access delay represents the service time of the system, it is used as the threshold condition to ensure that the queuing delay is within reasonable bounds. The paper describes the admission control algorithm and several simulation results are presented and discussed.

Automatic security classification by machine learning for cross-domain information exchange

Cross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found in a defense network is unlabeled, and usually new unlabeled information is produced every day. Humans find that doing the security labeling of such information is labor-intensive and time consuming. At the same time there is an information explosion observed where more and more unlabeled information is generated year by year. This calls for tools that can do advanced content inspection, and automatically determine the security label of an information object correspondingly. This paper presents a machine learning approach to this problem. To the best of our knowledge, machine learning has hardly been analyzed for this problem, and the analysis on topical classification presented here provides new knowledge and a basis for further work within this area. Presented results are promising and demonstrates that machine learning can become a useful tool to assist humans in determining the appropriate security label of an information object.

Building domain specific sentiment lexicons combining information from many sentiment lexicons and a domain specific corpus

Most approaches to sentiment analysis requires a sentiment lexicon in order to automatically predict sentiment or opinion in a text. The lexicon is generated by selecting words and assigning scores to the words, and the performance the sentiment analysis depends on the quality of the assigned scores. This paper addresses an aspect of sentiment lexicon generation that has been overlooked so far; namely that the most appropriate score assigned to a word in the lexicon is dependent on the domain. The common practice, on the contrary, is that the same lexicon is used without adjustments across different domains ignoring the fact that the scores are normally highly sensitive to the domain. Consequently, the same lexicon might perform well on a single domain while performing poorly on another domain, unless some score adjustment is performed. In this paper, we advocate that a sentiment lexicon needs some further adjustments in order to perform well in a specific domain. In order to cope with these domain specific adjustments, we adopt a stochastic formulation of the sentiment score assignment problem instead of the classical deterministic formulation. Thus, viewing a sentiment score as a stochastic variable permits us to accommodate to the domain specific adjustments. Experimental results demonstrate the feasibility of our approach and its superiority to generic lexicons without domain adjustments.

Improving classification of tweets using word-word co-occurrence information from a large external corpus

Classifying tweets is an intrinsically hard task as tweets are short messages which makes traditional bags of words based approach inefficient. In fact, bags of words approaches ignores relationships between important terms that do not co-occur literally. In this paper we resort to word-word co-occurence information from a large corpus to expand the vocabulary of another corpus consisting of tweets. Our results show that we are able to reduce the number of erroneous classifications by 14% using co-occurence information.

Improving Classification of Tweets Using Linguistic Information from a Large External Corpus

The bag of words representation of documents is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Improvements might be achieved by expanding the vocabulary with other relevant word, like synonyms.

On Enhancing the Label Propagation Algorithm for Sentiment Analysis Using Active Learning with an Artificial Oracle

A core component of Sentiment Analysis is the generation of sentiment lists. Label propagation is equivocally one of the most used approaches for generating sentiment lists based on annotated seed words in a manual manner. Words which are situated many hops away from the seed words tend to get low sentiment values. Such inherent property of the Label Propagation algorithm poses a controversial challenge in sentiment analysis. In this paper, we propose an iterative approach based on the theory of Active Learning [1] that attempts to remedy to this problem without any need for additional manual labeling. Our algorithm is bootstrapped with a limited amount of seeds. Then, at each iteration, a fixed number of “informative words” are selected as new seeds for labeling according to different criteria that we will elucidate in the paper. Subsequently, the Label Propagation is retrained in the next iteration with the additional labeled seeds. A major contribution of this article is that, unlike the theory of Active Learning that prompts the user for additional labeling, we generate the additional seeds with an Artificial Oracle. This is radically different from the main stream of Active Learning Theory that resorts to a human (user) as oracle for labeling those additional seeds. Consequently, we relieve the user from the cumbersome task of manual annotation while still achieving a high performance. The lexicons were evaluated by classifying product and movie reviews. Most of the generated sentiment lexicons using Active learning perform better than the Label Propagation algorithm.

A Simple and Efficient Algorithm for Lexicon Generation Inspired by Structural Balance Theory

Sentiment lexicon generation is a major task in the field of Sentiment Analysis. In contrast to the bulk of research that has focused almost exclusively on Label Propagation as primary tool for lexicon generation, we introduce a simple, yet efficient algorithm for lexicon generation that is inspired by Structural Balance Theory. Our algorithm is shown to outperform the classical Label Propagation algorithm. A major drawback of Label Propagation resides in the fact that words which are situated many hops away from the seed words tend to get low sentiment values since the inaccuracy in the synonym-relationship is not taken properly into account. In fact, a label of a word is simply the average of it is neighbours. To circumvent this problem, we propose a novel algorithm that supports better transitive sentiment polarity transferring from seed word to target words using the theory of Structural Balance theory. The premise of the algorithm is exemplified using the enemy of my enemy is my friend that preserves the transitivity structure captured by antonyms and synonyms. Thus, a low sentiment score is an indication of sentimental neutrality rather than due to the fact that the word in question is located at a far distance from the seeds. The lexicons based on thesauruses were built using different variants of our proposed algorithm. The lexicons were evaluated by classifying product and movie reviews and the results show satisfying classification performances that outperform Label Propagation. We consider Norwegian as a case study, but the algorithm be can easily applied to other languages.