Sentiment Analysis in the News (1309.6202v1)

Abstract: Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles - author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which (a) we test the relative suitability of various sentiment dictionaries and (b) we attempt to separate positive or negative opinion from good or bad news. In the experiments described here, we tested whether or not subject domain-defining vocabulary should be ignored. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.

Sentiment Analysis in News Articles: Challenges and Approaches

The paper, "Sentiment Analysis in the News," authored primarily by researchers from the University of Alicante and the European Commission – Joint Research Centre, addresses the unique challenges associated with sentiment analysis applied to news articles. This domain poses distinct issues compared to more traditional sentiment analysis in subjective text such as movie or product reviews. The primary difference lies in the complexity and diversity of targets within news articles, which often encompass multifaceted events and varying viewpoints from distinct sources.

Definition and Challenges

The authors identify significant challenges unique to news sentiment analysis, including the need to clearly define the target of sentiment, differentiation between sentiment expressed directly on the target versus sentiment clouded by broader news context, and the focus on explicitly stated opinions without necessitating complex interpretations. Furthermore, they introduce three viewpoints regarding news articles: those of the author, the reader, and the text itself. Each perspective requires distinct analytical approaches for effective sentiment detection.

Data and Methodology

The paper utilizes data from the EMM applications such as NewsBrief and MedISys, which categorize news into multiple subject domain classes. The researchers explored an innovative approach of excluding category-defining vocabulary from sentiment analysis—words that contribute to topic classification but overlap with sentiment lexicons. Through a series of experiments on English language news quotations, the paper evaluates the effectiveness of various sentiment dictionaries, including WordNet Affect, SentiWordNet, MicroWNOp, and an in-house resource termed JRC Tonality.

Experimental Outcomes

Remarkable results were achieved in identifying and annotating sentiments in quotations with the inter-annotator agreement reaching an impressive 81%, a significant improvement over the initial 50%. The authors note that more focused annotation guidelines, specifically highlighting the need to isolate sentiment on specific targets, facilitated this enhanced agreement.

The experimental focus on computing sentiment around entity mentions within varied word windows revealed that sentiment analysis performance significantly improved in narrower contexts (e.g., 6-word windows), contrasting whole-text sentiment calculations. JRC Tonality combined with MicroWN yielded the highest accuracy of 82%, underscoring the importance of lexicon selection and context scope in sentiment analysis models.

Implications and Future Directions

The paper successfully clarifies the complex task of sentiment analysis in news and formulates improved methodologies to tackle this. The suggested frameworks not only enhance the precision of sentiment polarity identification but also outline a pathway for further advancements. Future research directions could involve assessing the impact of incorporating negation and valence shifters and employing machine learning strategies or syntactic patterns to enhance sentiment identification accuracy. The authors also express intent to expand lexical resources across languages, facilitating cross-linguistic sentiment comparisons and temporal sentiment trend analysis.

In conclusion, this paper contributes significantly to the landscape of sentiment analysis in news, providing thoughtful insights and practical methodologies to address the domain-specific challenges effectively. As the field progresses, the strategies outlined in this paper will be pivotal in developing nuanced and sophisticated sentiment analysis systems with wider implications for media bias detection and automated opinion mining in diverse and multilingual contexts.