Abstract Detecting subjectivity expressed toward concerned targets is an interesting problem and has received intensive study. Previous work often treated each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets (eg, the subjectivity expressed toward two products or two political candidates in an election). In this paper, we relieve such an independence assumption in order to jointly model the subjectivity expressed toward multiple targets. We propose and show that an attention‐based encoder‐decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multitask learning, as well as models that independently predict subjectivity toward individual targets.

We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.

Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of distributed representation for subsequences as well as the sequences they form.An assumption in almost all the previous models, however, posits that the learned representation (e.g., a distributed representation for a sentence), is fully compositional from the atomic components (e.g., representations for words), while non-compositionality is a basic phenomenon in human languages.In this paper, we relieve the assumption by extending the chain-structured LSTM to directed acyclic graphs (DAGs), with the aim to endow linear-chain LSTMs with the capability of considering compositionality together with non-compositionality in the same semantic composition framework.From a more general viewpoint, the proposed models incorporate additional prior knowledge into recurrent neural networks, which is interesting to us, considering most NLP tasks have relatively small training data and appropriate prior knowledge could be beneficial to help cover missing semantics.Our experiments on sentiment composition demonstrate that the proposed models achieve the state-of-the-art performance, outperforming models that lack this ability.

The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process. We call the model S-LSTM, which provides a principled way of considering long-distance interaction over hierarchies, e.g., language or image parse structures. We leverage the models for semantic composition to understand the meaning of text, a fundamental problem in natural language understanding, and show that it outperforms a state-of-the-art recursive model by replacing its composition layers with the S-LSTM memory blocks. We also show that utilizing the given structures is helpful in achieving a performance better than that without considering the structures.