Certainly! Below you’ll find a table detailing specific improvements, section by section, following your earlier request and modeled on the recurring issues identified (AI-like language, vague adjectives, structure, tone, and academic rigor). For each “error” type, I provide the location (by section/chunk), the original excerpt, and a suggested academic/human rewrite. This will help you systematically edit your document. Error Correction Table Section/Chunk Location/Line or Phrase Issue Identified Suggested Change Introduction “significant events where firms disclose critical…” () Vague adjective “important events where firms disclose critical…” Background/Problem “A significant research gap prevails…” () Vague adjective “There is a notable research gap…” Background “pronounced information asymmetry…” () Vague adjective “information asymmetry…” Background “strong influences on retail investor perceptions…” () Vague adjective “can influence retail investor perceptions…” Literature Review “substantial evidence demonstrates…” () Vague adjective “a growing body of evidence demonstrates…” Data/Methodology “1. Select your company/companies of interest…” () – bullet/stepwise listing through Stepwise procedural Combine into narrative: “Companies of interest were selected based on regular earnings calls and data availability. Video and audio recordings of these disclosures were then sourced from official and public platforms…” Methodology Descriptions of FinBERT, OpenSmile, ReginaFace (–) – repetitive single-purpose introductions Redundant phrasing Introduce all models/tools together: “The analysis utilized FinBERT and GPT-4 for textual sentiment, OpenSmile for audio sentiment, and RetinaFace for facial sentiment, enabling cross-modal comparison.” Methodology “The idea is that if someone is being completely genuine…” (–) Conversational tone “Authentic communication is characterized by alignment among verbal, vocal, and facial emotional cues. Divergence among these can indicate impression management strategies.” Methodology “How do we measure these differences... 1. Each model... 2. The discrepancy score…” (–) Stepwise procedural “Sentiment scores obtained from each modality were compared to calculate the overall discrepancy between them.” Methodology “can be turned into time-based emotion graphs…” (–) Informal phrasing “These sentiment scores may be visualized as time-based graphs representing the progression of emotions throughout the recording.” Literature Review “Facial recognition technology, such as Amazon Web Services (AWS) Rekognition, automates the detection and quantification…” () Overly generic/ambig. “Facial recognition technology enables automated detection and quantification of executive emotional expressions during media appearances, aiding systematic analysis…” Methodology/Delimit. “pronounced information asymmetry, greater susceptibility…” (,) Vague adjective “information asymmetry, greater susceptibility…” Limitations Lack of critical or explicit limitations (should be at end of delimitations or methods;–) Missing analysis Add: “This study is limited by data availability and the exclusive focus on companies with full audio-visual and textual disclosures, possibly introducing sample bias. Tool-specific analytical limitations should also be considered.” Throughout Multiple stepwise or oversimplified explanations (see “3.4.1 Facial Expressions: ReginaFace”–) Pedagogical/AI-like Use concise prose, e.g., “Facial emotion recognition models process visual data frame by frame, detecting and classifying expressions to quantify executive affect during disclosures.” Example—Section by Section Introduction (–) Original: “Corporate earnings calls are significant events…” Change: “Corporate earnings calls are important events…” Original: “A significant research gap prevails…” Change: “There is a notable research gap…” Literature Review (,) Original: “In the context of developed markets, substantial evidence demonstrates…” Change: “In the context of developed markets, a growing body of evidence demonstrates…” Original: “Facial recognition technology, such as Amazon Web Services…” Change: “Facial recognition technology enables automated detection and quantification of executive emotional expressions during media appearances, aiding systematic analysis…” Background/Problem/Delimitations (–,,) Original: “strong influences on retail investor perceptions…” Change: “can influence retail investor perceptions…” Original: “pronounced information asymmetry…” Change: “information asymmetry…” Methodology/Data Collection (–,–) Original: Stepwise bullet list for data collection Change: Condensed narrative: “Companies of interest were selected based on data availability. Video, audio, and text disclosures were retrieved from official sources to ensure comprehensive sentiment analysis across modalities…” Original: Repetitive, tool-by-tool introductions Change: “Sentiment extraction leveraged FinBERT and GPT-4 for textual data, OpenSmile for audio data, and RetinaFace for facial sentiment, enabling a multimodal discrepancy analysis of executive communication…” Original: “The idea is that if someone is being completely genuine…” Change: “Authentic communication is characterized by congruence among verbal, vocal, and facial cues, with divergence potentially indicating impression management…” Additions—Limitations (–) Suggestion: Add a paragraph at the end of this section: “A key limitation of this study is the dependence on companies with publicly available, high-quality text, audio, and video data, possibly restricting the representativeness of the sample. Additionally, results may be sensitive to the specific sentiment models and thresholds selected for analysis.” How to Use This Table:Work through your document section by section, searching for each phrase or error pattern, and directly applying the corresponding suggested revision.If you want this in Word track changes or as an annotated PDF, just let me know!