Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to capture emotions that go beyond polarity. For these methods to work, they require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the range of emotions it captures diversity. In the past, sentiment analysis lexicons have been created by experts, such as linguists and behavioural scientists, with strict rules. Lexicon evaluation was also performed by experts or gold standards. In our paper, we propose a crowdsourcing method for lexicon acquisition, which is scalable, cost-effective, and doesn't require experts or gold standards. We also compare crowd and expert evaluations of the lexicon, to assess the overall lexicon quality, and the evaluation capabilities of the crowd.
Manual lexicon acquisition is constrained by the number of people contributing to the task, and the number of annotations from each participant. These constraints can be eliminated by increasing the number of people involved, for instance, by using crowdsourcing @cite_18 . Amazon's Mechanical Turk (MTurk) https: www.mturk.com is a crowdsourcing platform frequently used for polarity sentiment lexicon acquisition via crowdsourcing @cite_19 . MTurk is also used, for the annotation of one thousand tweets in @cite_28 , ten thousand terms in @cite_20 with gold standards, and the annotation of ninety five emoticons out of one thousand total emoticons found in @cite_17 . While @cite_27 had one thousand four hundred terms labelled with a supervised machine learning and crowd validators. The challenge is to introduce a work-flow that is scalable, unsupervised and applicable to different information types.
@cite_18 @cite_28 @cite_19 @cite_27 @cite_20 @cite_17
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Crowd-sourcing approaches such as Amazon's Mechanical Turk (MTurk) make it possible to annotate or collect large amounts of linguistic data at a relatively low cost and high speed. However, MTurk offers only limited control over who is allowed to particpate in a particular task. This is particularly problematic for tasks requiring free-form text entry. Unlike multiple-choice tasks there is no correct answer, and therefore control items for which the correct answer is known cannot be used. Furthermore, MTurk has no effective built-in mechanism to guarantee workers are proficient English writers. We describe our experience in creating corpora of images annotated with multiple one-sentence descriptions on MTurk and explore the effectiveness of different quality control strategies for collecting linguistic data using Mechanical MTurk. We find that the use of a qualification test provides the highest improvement of quality, whereas refining the annotations through follow-up tasks works rather poorly. Using our best setup, we construct two image corpora, totaling more than 40,000 descriptive captions for 9000 images. In this work we present results from using Amazon's Mechanical Turk (MTurk) to annotate translation lexicons between English and a large set of less commonly used languages. We generate candidate translations for 100 English words in each of 42 foreign languages using Wikipedia and a lexicon induction framework. We evaluate the MTurk annotations by using positive and negative control candidate translations. Additionally, we evaluate the annotations by adding pairs to our seed dictionaries, providing a feedback loop into the induction system. MTurk workers are more successful in annotating some languages than others and are not evenly distributed around the world or among the world's languages. However, in general, we find that MTurk is a valuable resource for gathering cheap and simple annotations for most of the languages that we explored, and these annotations provide useful feedback in building a larger, more accurate lexicon. Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80 of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2 higher accuracy (at the same price) or at 33 lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities. We propose a supervised model for expanding an opinion lexicon for Twitter.We combine automatically annotated tweets with existing hand-made opinion lexicons.We use POS tags and associations between words and sentiment as word-level features.Expanded words are mapped to a positive, negative, and neutral distribution.We outperform the performance obtained by using PMI semantic orientation alone. Opinion lexicons, which are lists of terms labeled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media.In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet.To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modeled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labeled by a seed lexicon formed by combining multiple hand-annotated lexicons.Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve significant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments.