Semantic feature activation takes time: longer SOA elicits earlier priming effects during reading

Abstract

While most previous studies of “semantic” priming confound associative and semantic relations, here we use a simple co-occurrence-based approach to examine “pure” semantic priming, while experimentally controlling for associative relations. We define associative relations by the co-occurrence of words in the sentences of a large text corpus. Contextual-semantic feature overlap, in contrast, is defined by the number of common associates that the prime shares with the target. Then we revisit the spreading activation theory and examine whether a long vs. short time available for semantic feature activation leads to early vs. late viewing time effects on the target words of a sentence reading experiment. We independently manipulate contextual-semantic feature overlap of two primes with one target word in sentences of the form pronoun, verb prime, article, adjective prime and target noun, e. g., "She rides the gray elephant." The results showed that long-SOA (verb-noun) overlap reduces early single and first fixation durations of the target noun, and short-SOA (adjective-noun) overlap reduces late go-past durations. This result pattern can be explained by the spreading activation theory: The semantic features of the prime words need some time to become sufficiently active before they can reliably affect target processing. Therefore, the verb can act on the target noun's early eye-movement measures presented three words later, while the adjective is presented immediately prior to the target—thus a difficult adjective-noun semantic integration leads to a late sentence re-examination of the preceding words.

Deep Learning-Based Text Sentiment Analysis in Chinese International Promotion

“…Guan et al [19] used a weakly supervised convolutional neural network for sentiment classification, and the framework consists of two steps: the first step learns a sentence representation that is weakly supervised by the overall score, and the second step uses sentences with labels for fine-tuning. To perform text sentiment analysis on the Twitter corpus, Zhao et al coupled potential contextual semantic linkages and co-occurrence statistical data between terms on Twitter with convolutional neural networks [20]. Wang et al used a long short-term memory network for Twitter sentiment classification by simulating word interactions in the synthesis process [21].…”