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Summary: Social networking websites such as Twitter provide a platform where users share their opinions about different news, events, and products. A recent research has identified that 81% of users search online first before purchasing products. Reviews are written in natural language and needs sentiment analysis for opinion extraction. Various approaches have been proposed to perform sentiment classification based on polarity bearing words in reviews such as noun, verb, adverb, and an adjective. Prior researchers have also identified the role of an adverb as a feature. However, impact analysis of adverb forms, are not yet studied and remains an open research area. This study focused on the following tasks: (1) impact of different forms of adverbs that are not studied for sentiment classification; (2) analysis of possible combinations of eight forms that are 255. The different forms are Adverb (RA), Degree Adverbs (RG), Degree Comparative Adverbs (RGR), General Adverbs (RR), General Comparative Adverbs (RRR), Locative Adverbs (RL), Prep. Adverb (RP), and Adverbs of time (RT); (3) comparison with benchmark dataset. Dataset of 5513 tweets is used to evaluate the idea. The findings of this work show that RRR and RR are important polarities bearing words for neutral opinions, RL for positive, and RP for negative opinions. [ABSTRACT FROM AUTHOR]

Većina postojećih poslužitelja koji nude geografske podatke sadrže brojčane podatke o različitim aspektima. Na rad na novom tipu geografskog poslužitelja potaknula su nas četiri glavna problema: (i) Kako se služiti brojkama kad različite baze podataka predstavljaju različite vrijednosti; (ii) Kako izgraditi velike zbirke slika s detaljnim opisima; (iii) Kako ažurirati informacije koje se brzo mijenjaju kao što su osobe na nekim važnim funkcijama te (iv) Kako opisati zemlje ne samo trivijalnim činjenicama, već tipičnim pričama za te zemlje. U prethodnim smo radovima raspravili i djelomično riješili probleme (i) i (ii). Odlučili smo riješiti (iii), regionalna ažuriranja, povezivanjem s međunarodnim konzorcijem čiji će članovi pomoći ili naći pojedince koji će to učiniti. Problemu (iv), kako stvoriti netrivijalne priče tipične za neku zemlju, pristupili smo ručno (konzorcij je do sada stvorio oko 200 priča) te razvijanjem tehnika za poluautomatsko stvaranje priča, što je tema ovoga rada. Osnovna je ideja bila prvo odrediti skupove pouzdanih poslužitelja koji se mogu razlikovati od regije do regije, izvući „zanimljive činjenice” iz njih i spojiti ih u sirovu verziju izvještaja koja će se obraditi ručno (zato ga nazivamo poluautomatskim). Izdvajanje „zanimljivih činjenica” s internetskih stranica može zvučati teško, no itekako je moguće odrediti heuristike koji će to učiniti, što nikad ne prelazi nekoliko redova za svrhu citiranja. Spomenimo kao primjer jedno vrlo jednostavno pravilo: Traži rečenice sa superlativima! Ako rečenica sadrži riječ kao što je „najveći”, „najviši”, „najimpresivniji” i si., ona vjerojatno sadrži neku zanimljivu činjenicu. S pomoću mašte uspjeli smo odrediti skup takvih pravila. Pokazat ćemo da priče mogu biti potpuno različite: u nekim zemljama dominiraju povijesne činjenice, u drugima ljepota krajolika, u trećima kulturna i ekonomska postignuća, u nekima neobične činjenice koje se odnose na dobitnike Nobelove nagrade, hranu, sport, druge aktivnosti, državne simbole, posebne zakone i si. Dobiveni rezultati mogu se provjeriti traženjem bilo koje zemlje u kategoriji „Posebne informacije” (Special Information) i „Iznenađujuće činjenice” (Surprising Facts). Svi primjeri opisani u ovom radu uzeti su arbitrarno iz skupine od 190 primjera kako bi se prikazalo kako sustav radi. Važno je spomenuti još dvije stvari: (a) riječ je o radnoj verziji koja je već prilično upotrebljiva; (b) osnovne ideje mogu se primijeniti na bilo koje područje. Geografija je izabrana s obzirom na velik broj podataka i interesa za to područje. Ako su naši algoritmi previdjeli neku važu činjenicu, to je manje važno nego da smo primijenili metode na vrste liječenja ili slično tome. [ABSTRACT FROM AUTHOR]

Most existing servers providing geographic data tend to offer various numeric data. We started to work on a new type of geographic server, motivated by four major issues: (i) How to handle figures when different databases present different values; (ii) How to build up sizeable collections of pictures with detailed descriptions; (iii) How to update rapidly changing information, such as personnel holding important functions, and (iv) how to describe countries not just by using trivial facts, but stories typical of the country involved. We have discussed and partially resolved issues (i) and (ii) in previous papers; we have decided to deal with (iii), regional updates, by tying in an international consortium whose members would either help themselves or find individuals to do so. It is issue (iv), how to generate non-trivial stories typical of a country, that we decided to tackle both manually (the consortium has by now generated around 200 stories), and by developing techniques for semi-automatic story generation, which is the topic of this paper. The basic idea was first to define sets of reasonably reliable servers that may differ from region to region, to extract "interesting facts" from the servers, and combine them in a raw version of a report that would require some manual cleaning-up (hence: semi-automatic). It may sound difficult to extract " interesting facts" from Web pages, but it is quite possible to define heuristics to do so, never exceeding the few lines allowed for quotation purposes. One very simple rule we adopted was this: 'Look for sentences with superlatives!' If a sentence contains words like " biggest" , " highest" , "most impressive" etc. it is likely to contain an interesting fact. With a little imagination, we have been able to establish a set of such rules. We will show that the stories can be completely different. For some countries, historical facts may dominate; for others, the beauty of landscapes; for others, cultural or economic achievements, and for yet others, unusual facts concerning Nobel Prize winners, food, entertainment, sports, other activities, national symbols, special laws, and so on. The results can be checked on by clicking on any country in the category "Special Information" under "Surprising Facts" . All examples shown in this paper were chosen fairly arbitrarily from over 190 examples, to show that the system is indeed working. There are two points to mention here: (a) it is a work in progress, yet has reached a very useable size; (b) the basic ideas can be applied to any area. The choice of geography was due to the wealth of data and interest in this area, but if our algorithms overlook some important facts, this is less critical than applied to types of medical treatment, etc. [ABSTRACT FROM AUTHOR]