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In this paper, the problem of a nonlinear model – specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between the data and the labels – exhibiting unstable performance depending on the hyperparameter under consideration.

There are three main optimization search methods for hyperparameter tuning: manual search, grid search and random search. This study shows that HUCRFs’ unstable performance depends on the hyperparameter values used and its performance is based on tuning that draws on grid and random searches. All experiments conducted used the n-gram features – specifically, unigram, bigram, and trigram.

Naturally, selecting a list of hyperparameter values based on a researchers’ experience to find a set in which the best performance is exhibited is better than finding it from a probability distribution. Realistically, however, it is impossible to calculate using the parameters in all combinations. The present research indicates that the random search method has a better performance compared with the grid search method while requiring shorter computation time and a reduced cost.

In this paper, the issues affecting the performance of HUCRF, a nonlinear model with performance that varies depending on the hyperparameters, but performs better than CRF, has been examined.

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

Departing from previous research which shows that abusive supervision, as a salient job demand, induces detrimental employee outcomes, this study examines how to create constructive consequences of abusive supervision. To do so, the authors identify the boundary conditions to change the negative effect of supervisory abuse on employees’ work engagement in a positive direction. The authors examine the interactive moderating effect of a personal resource (i.e. positive causal attribution of abusive supervision) and a job resource (i.e. workplace friendship) on the relationship between abusive supervision and work engagement.

The authors used data from a two-wave survey of 697 full-time workers with a time interval of one month and conducted three-way interaction analyses to test their hypothesized model.

Abusive supervision increases employees’ work engagement when they make a positive causal attribution of abusive supervision (i.e. interpreting their abusive supervisor’s motives as promoting their job performance, rather than as intentionally harming them) and have favorable workplace friends.

The authors study offers a novel picture of abusive supervision by revealing that supervisory abuse can enhance employees’ work engagement when it is coupled with proper personal and job resources. In addition, this study highlights that in order to identify constructive effects of abusive supervision, it is critical to delve into the interaction between resources from these two domains to deal with abusive supervision.