Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold

Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics' feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet "I love iPhone, but I hate iPad" can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier’s feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space

Social media and sentiment in bioenergy consultation

Purpose: The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach: This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings: Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications: Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Originality/value: Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity

Alleviating data sparsity for Twitter sentiment analysis

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentiment topic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches

Semantic smoothing for Twitter sentiment analysis

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifier from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character limit. In this work we propose using semantic smoothing to alleviate the data sparseness problem. Our approach extracts semantically hidden concepts from the training documents and then incorporates these concepts as additional features for classifier training. We tested our approach using two different methods. One is shallow semantic smoothing where words are replaced with their corresponding semantic concepts; another is to interpolate the original unigram language model in the Naive Bayes NB classifier with the generative model of words given semantic concepts. Preliminary results show that with shallow semantic smoothing the vocabulary size has been reduced by 20%. Moreover, the interpolation method improves upon shallow semantic smoothing by over 5% in sentiment classification and slightly outperforms NB trained on unigrams only without semantic smoothing

Semantic sentiment analysis of twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification

Quantising opinions for political tweets analysis

There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world

Dynamic analysis for kernel picking up and transporting on a pneumatic precision metering device for wheat

The objective of this study was to theoretically investigate the factors affecting kernels during picking up and transporting stage using a pneumatic precision metering device designed especially for wheat precision seeding and correlates findings with the results from practical testing under laboratory conditions using a test stand with camera system.  The results from dynamic analysis were found to be corresponding with that of the laboratory testing.  The findings revealed that the performance indices, such as quality of feed index (QFI), multiple index (MULI) and miss index (MISI), were obviously influenced by changing the negative pressure force FQ and rotating speed ω.  The result from test stand highlighted that when the negative pressure increased the QFI increased, MULI increased and MISI decreased, however, the QFI decreased and MISI increased with increasing the rotating speed.  The dynamic analysis likewise revealed that increasing the friction index tanαg by choosing a suitable material with high friction angle αg for seed plate as well as enlarging the seed hole diameter could improve the efficiency of the negative pressure force FQ.   Keywords: wheat, kernel, picking up, transportation, dynamic model, precision metering devic

Design and test of a pneumatic precision metering device for wheat

The objective of this study was to apply the precision metering on wheat seeding to overcome seed damage, seed loss and non-uniform distribution.  Accordingly, a prototype of the pneumatic precision metering device for wheat was developed.  The performance of the device, including quality of feed index (QFI), multiple index (MULI), miss index (MISI) and seed rate expressed in number of kernels per meter length (KPM), was investigated under laboratory conditions in Wuhan using a test stand with camera system.  The results revealed that the rotating speed (RS) and negative pressure (NP) and their interactions had a significant effect on these variables.  The maximum QFI (92.98%) was obtained at rotating speed of 19.0 rpm and negative pressures of 2.5 kPa with MULI and MISI of 2.01% and 5.09%, respectively.  However, the seed rate (KPM) was less than the recommended compared to previous hypothesis.  The best seed rate was 53 KPM producing QFI of 89.11% with MULI and MISI of 9.00% and 1.88%, respectively at rotating speed of 34 rpm and negative pressure of 4.5 kPa.  The recommended seed rates estimated at 40 KPM and 53 KPM for 12 cm and 15 cm row spacing respectively were achieved at a range of RS and NP with QFI ranging between 84.57 to 89.11%.  The study demonstrated that wheat could be seeding within an acceptable precisely range by pneumatic precision metering device. Keywords: wheat, experiments, performance indices, pneumatic precision metering device

(R)-1-Phenyl­ethanaminium (S)-4-chloro­mandelate

The absolute configuration of the title complex, C8H12N+·C8H6ClO3 − or [R-C6H5C(H)CH3NH3][S-4-ClC6H4C(H)(OH)CO2], has been confirmed by the structure determination. In the crystal structure, inter­molecular O—H⋯O and N—H⋯O hydrogen bonds form a two-dimensional network perpendicular to the c axis